Project
Loading the data
carbonData<-read.csv('/Users/angadsingh/Downloads/Carbon Emission.csv')
summary(carbonData) Body.Type Sex Diet How.Often.Shower Heating.Energy.Source Transport Vehicle.Type Social.Activity
Length:10000 Length:10000 Length:10000 Length:10000 Length:10000 Length:10000 Length:10000 Length:10000
Class :character Class :character Class :character Class :character Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character Mode :character Mode :character Mode :character Mode :character
Monthly.Grocery.Bill Frequency.of.Traveling.by.Air Vehicle.Monthly.Distance.Km Waste.Bag.Size Waste.Bag.Weekly.Count How.Long.TV.PC.Daily.Hour
Min. : 50.0 Length:10000 Min. : 0 Length:10000 Min. :1.000 Min. : 0.00
1st Qu.:111.0 Class :character 1st Qu.: 69 Class :character 1st Qu.:2.000 1st Qu.: 6.00
Median :173.0 Mode :character Median : 823 Mode :character Median :4.000 Median :12.00
Mean :173.9 Mean :2031 Mean :4.025 Mean :12.14
3rd Qu.:237.0 3rd Qu.:2517 3rd Qu.:6.000 3rd Qu.:18.00
Max. :299.0 Max. :9999 Max. :7.000 Max. :24.00
How.Many.New.Clothes.Monthly How.Long.Internet.Daily.Hour Energy.efficiency Recycling Cooking_With CarbonEmission
Min. : 0.00 Min. : 0.00 Length:10000 Length:10000 Length:10000 Min. : 306
1st Qu.:13.00 1st Qu.: 6.00 Class :character Class :character Class :character 1st Qu.:1538
Median :25.00 Median :12.00 Mode :character Mode :character Mode :character Median :2080
Mean :25.11 Mean :11.89 Mean :2269
3rd Qu.:38.00 3rd Qu.:18.00 3rd Qu.:2768
Max. :50.00 Max. :24.00 Max. :8377 str(carbonData)'data.frame': 10000 obs. of 20 variables:
$ Body.Type : chr "overweight" "obese" "overweight" "overweight" ...
$ Sex : chr "female" "female" "male" "male" ...
$ Diet : chr "pescatarian" "vegetarian" "omnivore" "omnivore" ...
$ How.Often.Shower : chr "daily" "less frequently" "more frequently" "twice a day" ...
$ Heating.Energy.Source : chr "coal" "natural gas" "wood" "wood" ...
$ Transport : chr "public" "walk/bicycle" "private" "walk/bicycle" ...
$ Vehicle.Type : chr "" "" "petrol" "" ...
$ Social.Activity : chr "often" "often" "never" "sometimes" ...
$ Monthly.Grocery.Bill : int 230 114 138 157 266 144 56 59 200 135 ...
$ Frequency.of.Traveling.by.Air: chr "frequently" "rarely" "never" "rarely" ...
$ Vehicle.Monthly.Distance.Km : int 210 9 2472 74 8457 658 5363 54 1376 440 ...
$ Waste.Bag.Size : chr "large" "extra large" "small" "medium" ...
$ Waste.Bag.Weekly.Count : int 4 3 1 3 1 1 4 3 3 1 ...
$ How.Long.TV.PC.Daily.Hour : int 7 9 14 20 3 22 9 5 3 8 ...
$ How.Many.New.Clothes.Monthly : int 26 38 47 5 5 18 11 39 31 23 ...
$ How.Long.Internet.Daily.Hour : int 1 5 6 7 6 9 19 15 15 18 ...
$ Energy.efficiency : chr "No" "No" "Sometimes" "Sometimes" ...
$ Recycling : chr "['Metal']" "['Metal']" "['Metal']" "['Paper', 'Plastic', 'Glass', 'Metal']" ...
$ Cooking_With : chr "['Stove', 'Oven']" "['Stove', 'Microwave']" "['Oven', 'Microwave']" "['Microwave', 'Grill', 'Airfryer']" ...
$ CarbonEmission : int 2238 1892 2595 1074 4743 1647 1832 2322 2494 1178 ...From the str of carbon data i can see that i am having empty vehicle types as “” so i will replace them with No vehicle
carbonData$Vehicle.Type[carbonData$Transport=='public'|carbonData$Transport=='walk/bicycle']<-'FuelEfficient'
#carbonData<- carbonData %>% mutate(Vehicle.Type=ifelse(Vehicle.Type=="","No vehicle",Vehicle.Type))
str(carbonData)'data.frame': 10000 obs. of 20 variables:
$ Body.Type : chr "overweight" "obese" "overweight" "overweight" ...
$ Sex : chr "female" "female" "male" "male" ...
$ Diet : chr "pescatarian" "vegetarian" "omnivore" "omnivore" ...
$ How.Often.Shower : chr "daily" "less frequently" "more frequently" "twice a day" ...
$ Heating.Energy.Source : chr "coal" "natural gas" "wood" "wood" ...
$ Transport : chr "public" "walk/bicycle" "private" "walk/bicycle" ...
$ Vehicle.Type : chr "FuelEfficient" "FuelEfficient" "petrol" "FuelEfficient" ...
$ Social.Activity : chr "often" "often" "never" "sometimes" ...
$ Monthly.Grocery.Bill : int 230 114 138 157 266 144 56 59 200 135 ...
$ Frequency.of.Traveling.by.Air: chr "frequently" "rarely" "never" "rarely" ...
$ Vehicle.Monthly.Distance.Km : int 210 9 2472 74 8457 658 5363 54 1376 440 ...
$ Waste.Bag.Size : chr "large" "extra large" "small" "medium" ...
$ Waste.Bag.Weekly.Count : int 4 3 1 3 1 1 4 3 3 1 ...
$ How.Long.TV.PC.Daily.Hour : int 7 9 14 20 3 22 9 5 3 8 ...
$ How.Many.New.Clothes.Monthly : int 26 38 47 5 5 18 11 39 31 23 ...
$ How.Long.Internet.Daily.Hour : int 1 5 6 7 6 9 19 15 15 18 ...
$ Energy.efficiency : chr "No" "No" "Sometimes" "Sometimes" ...
$ Recycling : chr "['Metal']" "['Metal']" "['Metal']" "['Paper', 'Plastic', 'Glass', 'Metal']" ...
$ Cooking_With : chr "['Stove', 'Oven']" "['Stove', 'Microwave']" "['Oven', 'Microwave']" "['Microwave', 'Grill', 'Airfryer']" ...
$ CarbonEmission : int 2238 1892 2595 1074 4743 1647 1832 2322 2494 1178 ...#carbonData[carbonData == ""]<-NA
colSums(is.na(carbonData)) Body.Type Sex Diet How.Often.Shower Heating.Energy.Source
0 0 0 0 0
Transport Vehicle.Type Social.Activity Monthly.Grocery.Bill Frequency.of.Traveling.by.Air
0 0 0 0 0
Vehicle.Monthly.Distance.Km Waste.Bag.Size Waste.Bag.Weekly.Count How.Long.TV.PC.Daily.Hour How.Many.New.Clothes.Monthly
0 0 0 0 0
How.Long.Internet.Daily.Hour Energy.efficiency Recycling Cooking_With CarbonEmission
0 0 0 0 0 library(dplyr)
carbonData<-carbonData %>%
mutate_if(is.character, as.factor)%>%
mutate_if(is.integer, as.numeric)
str(carbonData)'data.frame': 10000 obs. of 20 variables:
$ Body.Type : Factor w/ 4 levels "normal","obese",..: 3 2 3 3 2 3 4 4 3 4 ...
$ Sex : Factor w/ 2 levels "female","male": 1 1 2 2 1 2 1 1 2 1 ...
$ Diet : Factor w/ 4 levels "omnivore","pescatarian",..: 2 4 1 1 4 4 3 3 1 2 ...
$ How.Often.Shower : Factor w/ 4 levels "daily","less frequently",..: 1 2 3 4 1 2 2 3 1 1 ...
$ Heating.Energy.Source : Factor w/ 4 levels "coal","electricity",..: 1 3 4 4 1 4 4 1 4 4 ...
$ Transport : Factor w/ 3 levels "private","public",..: 2 3 1 3 1 2 1 3 2 2 ...
$ Vehicle.Type : Factor w/ 6 levels "diesel","electric",..: 3 3 6 3 1 3 4 3 3 3 ...
$ Social.Activity : Factor w/ 3 levels "never","often",..: 2 2 1 3 2 3 1 3 1 2 ...
$ Monthly.Grocery.Bill : num 230 114 138 157 266 144 56 59 200 135 ...
$ Frequency.of.Traveling.by.Air: Factor w/ 4 levels "frequently","never",..: 1 3 2 3 4 1 3 4 1 3 ...
$ Vehicle.Monthly.Distance.Km : num 210 9 2472 74 8457 ...
$ Waste.Bag.Size : Factor w/ 4 levels "extra large",..: 2 1 4 3 2 2 3 1 3 1 ...
$ Waste.Bag.Weekly.Count : num 4 3 1 3 1 1 4 3 3 1 ...
$ How.Long.TV.PC.Daily.Hour : num 7 9 14 20 3 22 9 5 3 8 ...
$ How.Many.New.Clothes.Monthly : num 26 38 47 5 5 18 11 39 31 23 ...
$ How.Long.Internet.Daily.Hour : num 1 5 6 7 6 9 19 15 15 18 ...
$ Energy.efficiency : Factor w/ 3 levels "No","Sometimes",..: 1 1 2 2 3 2 2 1 3 2 ...
$ Recycling : Factor w/ 16 levels "['Glass', 'Metal']",..: 3 3 3 7 11 4 16 8 2 2 ...
$ Cooking_With : Factor w/ 16 levels "['Grill', 'Airfryer']",..: 14 10 6 2 7 13 1 10 2 2 ...
$ CarbonEmission : num 2238 1892 2595 1074 4743 ...summary(carbonData) Body.Type Sex Diet How.Often.Shower Heating.Energy.Source Transport Vehicle.Type Social.Activity
normal :2473 female:5007 omnivore :2492 daily :2546 coal :2523 private :3279 diesel : 622 never :3406
obese :2500 male :4993 pescatarian:2554 less frequently:2487 electricity:2552 public :3294 electric : 671 often :3319
overweight :2487 vegan :2497 more frequently:2451 natural gas:2462 walk/bicycle:3427 FuelEfficient:6721 sometimes:3275
underweight:2540 vegetarian :2457 twice a day :2516 wood :2463 hybrid : 642
lpg : 697
petrol : 647
Monthly.Grocery.Bill Frequency.of.Traveling.by.Air Vehicle.Monthly.Distance.Km Waste.Bag.Size Waste.Bag.Weekly.Count How.Long.TV.PC.Daily.Hour
Min. : 50.0 frequently :2524 Min. : 0 extra large:2500 Min. :1.000 Min. : 0.00
1st Qu.:111.0 never :2459 1st Qu.: 69 large :2501 1st Qu.:2.000 1st Qu.: 6.00
Median :173.0 rarely :2477 Median : 823 medium :2474 Median :4.000 Median :12.00
Mean :173.9 very frequently:2540 Mean :2031 small :2525 Mean :4.025 Mean :12.14
3rd Qu.:237.0 3rd Qu.:2517 3rd Qu.:6.000 3rd Qu.:18.00
Max. :299.0 Max. :9999 Max. :7.000 Max. :24.00
How.Many.New.Clothes.Monthly How.Long.Internet.Daily.Hour Energy.efficiency Recycling
Min. : 0.00 Min. : 0.00 No :3221 [] : 675
1st Qu.:13.00 1st Qu.: 6.00 Sometimes:3463 ['Paper', 'Plastic', 'Metal'] : 648
Median :25.00 Median :12.00 Yes :3316 ['Paper', 'Glass', 'Metal'] : 647
Mean :25.11 Mean :11.89 ['Glass', 'Metal'] : 645
3rd Qu.:38.00 3rd Qu.:18.00 ['Paper', 'Plastic', 'Glass', 'Metal']: 637
Max. :50.00 Max. :24.00 ['Paper', 'Plastic'] : 633
(Other) :6115
Cooking_With CarbonEmission
['Stove', 'Oven'] : 670 Min. : 306
['Stove', 'Microwave', 'Grill', 'Airfryer'] : 652 1st Qu.:1538
['Oven', 'Microwave'] : 649 Median :2080
['Oven', 'Microwave', 'Grill', 'Airfryer'] : 638 Mean :2269
['Stove', 'Oven', 'Microwave', 'Grill', 'Airfryer']: 637 3rd Qu.:2768
['Stove', 'Grill', 'Airfryer'] : 628 Max. :8377
(Other) :6126 table(carbonData$Body.Type)
normal obese overweight underweight
2473 2500 2487 2540 table(carbonData$Sex)
female male
5007 4993 table(carbonData$Diet)
omnivore pescatarian vegan vegetarian
2492 2554 2497 2457 table(carbonData$How.Often.Shower)
daily less frequently more frequently twice a day
2546 2487 2451 2516 table(carbonData$Heating.Energy.Source)
coal electricity natural gas wood
2523 2552 2462 2463 table(carbonData$Transport)
private public walk/bicycle
3279 3294 3427 table(carbonData$Social.Activity)
never often sometimes
3406 3319 3275 table(carbonData$Frequency.of.Traveling.by.Air)
frequently never rarely very frequently
2524 2459 2477 2540 table(carbonData$Waste.Bag.Size)
extra large large medium small
2500 2501 2474 2525 table(carbonData$Energy.efficiency)
No Sometimes Yes
3221 3463 3316 hist(carbonData$CarbonEmission)
carbonData$CarbonEmission<-log(carbonData$CarbonEmission)
hist(carbonData$CarbonEmission)
carbonIndices<-which(names(carbonData)=='CarbonEmission')
for (c in colnames(carbonData[,-carbonIndices])) {
if(is.factor(carbonData[,c])){
try({
anovaaResult<-aov(carbonData$CarbonEmission~carbonData[,c])
cat("ANOVA of ",c, "and CarbonEmission", "\n")
print(summary(anovaaResult))
boxplot(carbonData$CarbonEmission~carbonData[,c],shade=TRUE, main = paste("Carbon Emission vs", c), xlab ="CarbonEmission", ylab=c ,col="lightgreen")
})
}
else if (is.numeric(carbonData[,c])){
try({
corTest<-cor.test(carbonData$CarbonEmission,carbonData[,c], method = "pearson")
cat("p.value of ",c, "and Carbon Emission", corTest$p.value, "\n")
plot(carbonData$CarbonEmission,carbonData[,c], main = paste("Carbon Emission vs", c), xlab ="Carbon Emission", ylab=c)
})
}
}ANOVA of Body.Type and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 3 82.7 27.583 149 <2e-16 ***
Residuals 9996 1850.3 0.185
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ANOVA of Sex and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 1 55.5 55.51 295.6 <2e-16 ***
Residuals 9998 1877.5 0.19
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ANOVA of Diet and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 3 11.8 3.944 20.52 2.98e-13 ***
Residuals 9996 1921.2 0.192
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ANOVA of How.Often.Shower and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 3 1.9 0.6333 3.278 0.0201 *
Residuals 9996 1931.1 0.1932
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ANOVA of Heating.Energy.Source and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 3 71.1 23.702 127.2 <2e-16 ***
Residuals 9996 1861.9 0.186
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ANOVA of Transport and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 2 384.7 192.34 1242 <2e-16 ***
Residuals 9997 1548.3 0.15
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ANOVA of Vehicle.Type and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 5 573.6 114.71 843.3 <2e-16 ***
Residuals 9994 1359.5 0.14
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ANOVA of Social.Activity and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 2 8.5 4.248 22.07 2.74e-10 ***
Residuals 9997 1924.5 0.193
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
p.value of Monthly.Grocery.Bill and Carbon Emission 8.380793e-21 
ANOVA of Frequency.of.Traveling.by.Air and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 3 568.2 189.41 1387 <2e-16 ***
Residuals 9996 1364.8 0.14
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
p.value of Vehicle.Monthly.Distance.Km and Carbon Emission 0 
ANOVA of Waste.Bag.Size and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 3 53.1 17.692 94.07 <2e-16 ***
Residuals 9996 1879.9 0.188
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
p.value of Waste.Bag.Weekly.Count and Carbon Emission 3.257926e-79 
p.value of How.Long.TV.PC.Daily.Hour and Carbon Emission 0.2282313 
p.value of How.Many.New.Clothes.Monthly and Carbon Emission 8.851718e-131 
p.value of How.Long.Internet.Daily.Hour and Carbon Emission 1.411988e-09 
ANOVA of Energy.efficiency and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 2 0.7 0.3284 1.699 0.183
Residuals 9997 1932.4 0.1933 
ANOVA of Recycling and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 15 41.7 2.7802 14.68 <2e-16 ***
Residuals 9984 1891.3 0.1894
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ANOVA of Cooking_With and CarbonEmission
Df Sum Sq Mean Sq F value Pr(>F)
carbonData[, c] 15 5 0.3301 1.709 0.0422 *
Residuals 9984 1928 0.1931
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
library(tidyverse)
parseList<-function(x,emptyLable){
parsedItem<-str_remove_all(x,"\\[|\\]|'")%>%
strsplit(", ")%>%
unlist()
if(length(parsedItem)==0||all(parsedItem=="")){
parsedItem<-emptyLable
}
return(parsedItem)
}
carbonData$Recycling<-sapply(carbonData$Recycling,parseList,emptyLable="No_Recycling")
carbonData$Cooking_With<-sapply(carbonData$Cooking_With,parseList,emptyLable="No_Cooking")
carbonData$Recycling<-sapply(carbonData$Recycling,paste,collapse=",")
carbonData$Cooking_With<-sapply(carbonData$Cooking_With,paste,collapse=",")
#str(carbonData)
dummies<-function(col){
items<-unlist(str_split(col,","))
items<-trimws(items)
items<-items[items != ""]
uniqueItems<-unique(c(items))
dummyDataFrame<-data.frame(matrix(0,nrow = length(col),ncol = length(uniqueItems)))
colnames(dummyDataFrame)<-uniqueItems
for (i in seq_along(col)) {
rowItems<-unlist(str_split(col[i],","))%>%
map_chr(~str_trim(.))%>%
discard(~.=="")
rowItems<-rowItems[rowItems %in% uniqueItems]
dummyDataFrame[i,rowItems]<-1
}
return(dummyDataFrame)
}
recyclingDummies<-dummies(carbonData$Recycling)
cookingDummies<-dummies(carbonData$Cooking_With)
carbonData<-cbind(carbonData,recyclingDummies,cookingDummies)
carbonData$Recycling<- NULL
carbonData$Cooking_With<-NULL
str(carbonData)'data.frame': 10000 obs. of 29 variables:
$ Body.Type : Factor w/ 4 levels "normal","obese",..: 3 2 3 3 2 3 4 4 3 4 ...
$ Sex : Factor w/ 2 levels "female","male": 1 1 2 2 1 2 1 1 2 1 ...
$ Diet : Factor w/ 4 levels "omnivore","pescatarian",..: 2 4 1 1 4 4 3 3 1 2 ...
$ How.Often.Shower : Factor w/ 4 levels "daily","less frequently",..: 1 2 3 4 1 2 2 3 1 1 ...
$ Heating.Energy.Source : Factor w/ 4 levels "coal","electricity",..: 1 3 4 4 1 4 4 1 4 4 ...
$ Transport : Factor w/ 3 levels "private","public",..: 2 3 1 3 1 2 1 3 2 2 ...
$ Vehicle.Type : Factor w/ 6 levels "diesel","electric",..: 3 3 6 3 1 3 4 3 3 3 ...
$ Social.Activity : Factor w/ 3 levels "never","often",..: 2 2 1 3 2 3 1 3 1 2 ...
$ Monthly.Grocery.Bill : num 230 114 138 157 266 144 56 59 200 135 ...
$ Frequency.of.Traveling.by.Air: Factor w/ 4 levels "frequently","never",..: 1 3 2 3 4 1 3 4 1 3 ...
$ Vehicle.Monthly.Distance.Km : num 210 9 2472 74 8457 ...
$ Waste.Bag.Size : Factor w/ 4 levels "extra large",..: 2 1 4 3 2 2 3 1 3 1 ...
$ Waste.Bag.Weekly.Count : num 4 3 1 3 1 1 4 3 3 1 ...
$ How.Long.TV.PC.Daily.Hour : num 7 9 14 20 3 22 9 5 3 8 ...
$ How.Many.New.Clothes.Monthly : num 26 38 47 5 5 18 11 39 31 23 ...
$ How.Long.Internet.Daily.Hour : num 1 5 6 7 6 9 19 15 15 18 ...
$ Energy.efficiency : Factor w/ 3 levels "No","Sometimes",..: 1 1 2 2 3 2 2 1 3 2 ...
$ CarbonEmission : num 7.71 7.55 7.86 6.98 8.46 ...
$ Metal : num 1 1 1 1 0 1 0 0 0 0 ...
$ Paper : num 0 0 0 1 1 1 0 1 0 0 ...
$ Plastic : num 0 0 0 1 0 0 0 1 0 0 ...
$ Glass : num 0 0 0 1 0 1 0 1 1 1 ...
$ No_Recycling : num 0 0 0 0 0 0 1 0 0 0 ...
$ Stove : num 1 1 0 0 0 1 0 1 0 0 ...
$ Oven : num 1 0 1 0 1 1 0 0 0 0 ...
$ Microwave : num 0 1 1 1 0 1 0 1 1 1 ...
$ Grill : num 0 0 0 1 0 0 1 0 1 1 ...
$ Airfryer : num 0 0 0 1 0 0 1 0 1 1 ...
$ No_Cooking : num 0 0 0 0 0 0 0 0 0 0 ...library(caret)
carbonDataIndexs <- createDataPartition(carbonData$CarbonEmission, p=0.8, list=FALSE)
carbonTrainData<-carbonData[carbonDataIndexs,]
carbonTrainData
carbonTestData<-carbonData[-carbonDataIndexs,]
carbonTestData
carbonTestLabels<-carbonTestData$CarbonEmission#Benchmark
meanTransport<- carbonTrainData %>% #this will calculate emission per transport type
group_by(Transport) %>%
summarize(meanEmission= mean(CarbonEmission, na.rm = TRUE))
meanTransportTable<-setNames(meanTransport$meanEmission,meanTransport$Transport) # will store transport levels to its average emission
predictTransport<-function(row){
transportType<-as.character(row["Transport"])
if(transportType %in% names(meanTransportTable)){
return(meanTransportTable[[transportType]])
}
}
benchmarkPred<-apply(carbonTestData,1,predictTransport) # will apply predictTransport function on the test data
rmse(benchmarkPred,carbonTestData$CarbonEmission)[1] 0.001769568MAE(benchmarkPred,carbonTestData$CarbonEmission)[1] 0.3201579rsquaredNew<-sum((benchmarkPred-carbonTestData$CarbonEmission)^2)/sum((carbonTestData$CarbonEmission-mean(carbonTestData$CarbonEmission))^2)
rsquaredNew[1] 0.8108719#KNN Model
knnModel<-train(CarbonEmission~.,data = carbonTrainData, method="knn", trControl=trainControl(method = "cv", number=5))knnModelk-Nearest Neighbors
8001 samples
28 predictor
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 6402, 6400, 6401, 6400, 6401
Resampling results across tuning parameters:
k RMSE Rsquared MAE
5 0.3935491 0.2364802 0.3108153
7 0.3847708 0.2561085 0.3047360
9 0.3814251 0.2635529 0.3024925
RMSE was used to select the optimal model using the smallest value.
The final value used for the model was k = 9.knnPred<-predict(knnModel,newdata = carbonTestData)
rmse=function(x,y){
return((mean(x-y)^2)^0.5)
}
rmse(knnPred,carbonTestLabels)[1] 0.008526172lmModel<-train(CarbonEmission~.,data = carbonTrainData, method="lm", trControl=trainControl(method = "cv", number=5))
lmModelLinear Regression
8001 samples
28 predictor
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 6402, 6402, 6400, 6400, 6400
Resampling results:
RMSE Rsquared MAE
0.1210893 0.9245749 0.08667648
Tuning parameter 'intercept' was held constant at a value of TRUEsummary(lmModel)
Call:
lm(formula = .outcome ~ ., data = dat)
Residuals:
Min 1Q Median 3Q Max
-0.77857 -0.05514 0.00892 0.06598 0.46663
Coefficients: (2 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.421e+00 1.191e-02 623.323 < 2e-16 ***
Body.Typeobese 1.878e-01 3.817e-03 49.202 < 2e-16 ***
Body.Typeoverweight 9.329e-02 3.845e-03 24.262 < 2e-16 ***
Body.Typeunderweight -4.985e-02 3.810e-03 -13.084 < 2e-16 ***
Sexmale 1.515e-01 2.707e-03 55.959 < 2e-16 ***
Dietpescatarian -4.321e-02 3.796e-03 -11.382 < 2e-16 ***
Dietvegan -7.942e-02 3.830e-03 -20.737 < 2e-16 ***
Dietvegetarian -6.956e-02 3.845e-03 -18.091 < 2e-16 ***
`How.Often.Showerless frequently` -1.128e-02 3.794e-03 -2.972 0.00296 **
`How.Often.Showermore frequently` 1.626e-02 3.821e-03 4.255 2.11e-05 ***
`How.Often.Showertwice a day` 9.034e-03 3.811e-03 2.371 0.01778 *
Heating.Energy.Sourceelectricity -2.227e-01 3.800e-03 -58.619 < 2e-16 ***
`Heating.Energy.Sourcenatural gas` -9.763e-02 3.845e-03 -25.392 < 2e-16 ***
Heating.Energy.Sourcewood -9.692e-02 3.830e-03 -25.306 < 2e-16 ***
Transportpublic -1.955e-01 6.729e-03 -29.057 < 2e-16 ***
`Transportwalk/bicycle` -1.719e-01 7.130e-03 -24.114 < 2e-16 ***
Vehicle.Typeelectric -5.084e-01 7.470e-03 -68.059 < 2e-16 ***
Vehicle.TypeFuelEfficient NA NA NA NA
Vehicle.Typehybrid -1.313e-01 7.552e-03 -17.386 < 2e-16 ***
Vehicle.Typelpg 4.272e-02 7.481e-03 5.710 1.17e-08 ***
Vehicle.Typepetrol 2.001e-01 7.577e-03 26.412 < 2e-16 ***
Social.Activityoften 8.611e-02 3.296e-03 26.128 < 2e-16 ***
Social.Activitysometimes 4.219e-02 3.311e-03 12.744 < 2e-16 ***
Monthly.Grocery.Bill 4.704e-04 1.874e-05 25.103 < 2e-16 ***
Frequency.of.Traveling.by.Airnever -3.609e-01 3.835e-03 -94.111 < 2e-16 ***
Frequency.of.Traveling.by.Airrarely -2.434e-01 3.829e-03 -63.570 < 2e-16 ***
`Frequency.of.Traveling.by.Airvery frequently` 2.669e-01 3.810e-03 70.052 < 2e-16 ***
Vehicle.Monthly.Distance.Km 6.660e-05 8.021e-07 83.036 < 2e-16 ***
Waste.Bag.Sizelarge -6.449e-02 3.816e-03 -16.898 < 2e-16 ***
Waste.Bag.Sizemedium -1.297e-01 3.841e-03 -33.763 < 2e-16 ***
Waste.Bag.Sizesmall -2.006e-01 3.828e-03 -52.405 < 2e-16 ***
Waste.Bag.Weekly.Count 4.115e-02 6.792e-04 60.592 < 2e-16 ***
How.Long.TV.PC.Daily.Hour 1.192e-03 1.900e-04 6.272 3.74e-10 ***
How.Many.New.Clothes.Monthly 7.133e-03 9.174e-05 77.746 < 2e-16 ***
How.Long.Internet.Daily.Hour 4.046e-03 1.853e-04 21.829 < 2e-16 ***
Energy.efficiencySometimes -1.997e-02 3.304e-03 -6.043 1.58e-09 ***
Energy.efficiencyYes -3.255e-02 3.353e-03 -9.708 < 2e-16 ***
Metal -7.208e-02 2.839e-03 -25.394 < 2e-16 ***
Paper -7.743e-02 2.835e-03 -27.309 < 2e-16 ***
Plastic -3.004e-02 2.834e-03 -10.602 < 2e-16 ***
Glass -5.130e-02 2.831e-03 -18.117 < 2e-16 ***
No_Recycling -1.334e-02 6.409e-03 -2.081 0.03749 *
Stove 1.266e-02 2.825e-03 4.482 7.49e-06 ***
Oven 1.488e-02 2.829e-03 5.260 1.47e-07 ***
Microwave 5.386e-03 2.822e-03 1.909 0.05632 .
Grill 1.820e-02 2.817e-03 6.458 1.12e-10 ***
Airfryer NA NA NA NA
No_Cooking -1.236e-02 6.608e-03 -1.870 0.06150 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1207 on 7955 degrees of freedom
Multiple R-squared: 0.9256, Adjusted R-squared: 0.9252
F-statistic: 2199 on 45 and 7955 DF, p-value: < 2.2e-16stepwiseModel<-train(CarbonEmission~.,data = carbonTrainData, method="leapBackward", trControl=trainControl(method = "cv", number=5))Warning: 2 linear dependencies foundReordering variables and trying again:Warning: 2 linear dependencies foundReordering variables and trying again:Warning: 2 linear dependencies foundReordering variables and trying again:Warning: 2 linear dependencies foundReordering variables and trying again:Warning: 2 linear dependencies foundReordering variables and trying again:Warning: 2 linear dependencies foundReordering variables and trying again:stepwiseModelLinear Regression with Backwards Selection
8001 samples
28 predictor
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 6401, 6400, 6402, 6402, 6399
Resampling results across tuning parameters:
nvmax RMSE Rsquared MAE
2 0.3782825 0.2651774 0.3006870
3 0.3607228 0.3315503 0.2868409
4 0.3471377 0.3809492 0.2731293
RMSE was used to select the optimal model using the smallest value.
The final value used for the model was nvmax = 4.summary(stepwiseModel$finalModel)Subset selection object
47 Variables (and intercept)
Forced in Forced out
Body.Typeobese FALSE FALSE
Body.Typeoverweight FALSE FALSE
Body.Typeunderweight FALSE FALSE
Sexmale FALSE FALSE
Dietpescatarian FALSE FALSE
Dietvegan FALSE FALSE
Dietvegetarian FALSE FALSE
How.Often.Showerless frequently FALSE FALSE
How.Often.Showermore frequently FALSE FALSE
How.Often.Showertwice a day FALSE FALSE
Heating.Energy.Sourceelectricity FALSE FALSE
Heating.Energy.Sourcenatural gas FALSE FALSE
Heating.Energy.Sourcewood FALSE FALSE
Transportpublic FALSE FALSE
Transportwalk/bicycle FALSE FALSE
Vehicle.Typeelectric FALSE FALSE
Vehicle.Typehybrid FALSE FALSE
Vehicle.Typelpg FALSE FALSE
Vehicle.Typepetrol FALSE FALSE
Social.Activityoften FALSE FALSE
Social.Activitysometimes FALSE FALSE
Monthly.Grocery.Bill FALSE FALSE
Frequency.of.Traveling.by.Airnever FALSE FALSE
Frequency.of.Traveling.by.Airrarely FALSE FALSE
Frequency.of.Traveling.by.Airvery frequently FALSE FALSE
Vehicle.Monthly.Distance.Km FALSE FALSE
Waste.Bag.Sizelarge FALSE FALSE
Waste.Bag.Sizemedium FALSE FALSE
Waste.Bag.Sizesmall FALSE FALSE
Waste.Bag.Weekly.Count FALSE FALSE
How.Long.TV.PC.Daily.Hour FALSE FALSE
How.Many.New.Clothes.Monthly FALSE FALSE
How.Long.Internet.Daily.Hour FALSE FALSE
Energy.efficiencySometimes FALSE FALSE
Energy.efficiencyYes FALSE FALSE
Metal FALSE FALSE
Paper FALSE FALSE
Plastic FALSE FALSE
Glass FALSE FALSE
No_Recycling FALSE FALSE
Stove FALSE FALSE
Oven FALSE FALSE
Microwave FALSE FALSE
Grill FALSE FALSE
No_Cooking FALSE FALSE
Vehicle.TypeFuelEfficient FALSE FALSE
Airfryer FALSE FALSE
1 subsets of each size up to 5
Selection Algorithm: backward
Body.Typeobese Body.Typeoverweight Body.Typeunderweight Sexmale Dietpescatarian Dietvegan Dietvegetarian How.Often.Showerless frequently
1 ( 1 ) " " " " " " " " " " " " " " " "
2 ( 1 ) " " " " " " " " " " " " " " " "
3 ( 1 ) " " " " " " " " " " " " " " " "
4 ( 1 ) " " " " " " " " " " " " " " " "
5 ( 1 ) " " " " " " " " " " " " " " " "
How.Often.Showermore frequently How.Often.Showertwice a day Heating.Energy.Sourceelectricity Heating.Energy.Sourcenatural gas
1 ( 1 ) " " " " " " " "
2 ( 1 ) " " " " " " " "
3 ( 1 ) " " " " " " " "
4 ( 1 ) " " " " " " " "
5 ( 1 ) " " " " " " " "
Heating.Energy.Sourcewood Transportpublic Transportwalk/bicycle Vehicle.Typeelectric Vehicle.TypeFuelEfficient Vehicle.Typehybrid Vehicle.Typelpg
1 ( 1 ) " " " " " " " " " " " " " "
2 ( 1 ) " " " " " " " " " " " " " "
3 ( 1 ) " " " " " " "*" " " " " " "
4 ( 1 ) " " " " " " "*" " " " " " "
5 ( 1 ) " " " " " " "*" " " " " " "
Vehicle.Typepetrol Social.Activityoften Social.Activitysometimes Monthly.Grocery.Bill Frequency.of.Traveling.by.Airnever
1 ( 1 ) " " " " " " " " " "
2 ( 1 ) " " " " " " " " " "
3 ( 1 ) " " " " " " " " " "
4 ( 1 ) " " " " " " " " " "
5 ( 1 ) " " " " " " " " "*"
Frequency.of.Traveling.by.Airrarely Frequency.of.Traveling.by.Airvery frequently Vehicle.Monthly.Distance.Km Waste.Bag.Sizelarge
1 ( 1 ) " " " " "*" " "
2 ( 1 ) " " "*" "*" " "
3 ( 1 ) " " "*" "*" " "
4 ( 1 ) " " "*" "*" " "
5 ( 1 ) " " "*" "*" " "
Waste.Bag.Sizemedium Waste.Bag.Sizesmall Waste.Bag.Weekly.Count How.Long.TV.PC.Daily.Hour How.Many.New.Clothes.Monthly How.Long.Internet.Daily.Hour
1 ( 1 ) " " " " " " " " " " " "
2 ( 1 ) " " " " " " " " " " " "
3 ( 1 ) " " " " " " " " " " " "
4 ( 1 ) " " " " " " " " "*" " "
5 ( 1 ) " " " " " " " " "*" " "
Energy.efficiencySometimes Energy.efficiencyYes Metal Paper Plastic Glass No_Recycling Stove Oven Microwave Grill Airfryer No_Cooking
1 ( 1 ) " " " " " " " " " " " " " " " " " " " " " " " " " "
2 ( 1 ) " " " " " " " " " " " " " " " " " " " " " " " " " "
3 ( 1 ) " " " " " " " " " " " " " " " " " " " " " " " " " "
4 ( 1 ) " " " " " " " " " " " " " " " " " " " " " " " " " "
5 ( 1 ) " " " " " " " " " " " " " " " " " " " " " " " " " " #Lasso Model
library(glmnet)
set.seed(1)
lassoModel<-train(CarbonEmission~.,data = carbonTrainData,method="glmnet",trControl= trainControl(method = "cv", number=5), tuneGrid = expand.grid(alpha=1, lambda=10^seq(-3,3,length=100))) Warning: There were missing values in resampled performance measures.lassoModelglmnet
8001 samples
28 predictor
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 6400, 6402, 6400, 6401, 6401
Resampling results across tuning parameters:
lambda RMSE Rsquared MAE
1.000000e-03 0.1214973 0.9245000 0.08687629
1.149757e-03 0.1215854 0.9244221 0.08694724
1.321941e-03 0.1217004 0.9243204 0.08703862
1.519911e-03 0.1218552 0.9241830 0.08716368
1.747528e-03 0.1220575 0.9240036 0.08732665
2.009233e-03 0.1223224 0.9237676 0.08754325
2.310130e-03 0.1226701 0.9234560 0.08783140
2.656088e-03 0.1231219 0.9230482 0.08820983
3.053856e-03 0.1237161 0.9225053 0.08870755
3.511192e-03 0.1244955 0.9217826 0.08936085
4.037017e-03 0.1255163 0.9208163 0.09024974
4.641589e-03 0.1268402 0.9195356 0.09140592
5.336699e-03 0.1285144 0.9178867 0.09289531
6.135907e-03 0.1306158 0.9157707 0.09473980
7.054802e-03 0.1331601 0.9131792 0.09695460
8.111308e-03 0.1363159 0.9098632 0.09969136
9.326033e-03 0.1400410 0.9058695 0.10288492
1.072267e-02 0.1440491 0.9016682 0.10627227
1.232847e-02 0.1488871 0.8964030 0.11039431
1.417474e-02 0.1542083 0.8906494 0.11481353
1.629751e-02 0.1592735 0.8859180 0.11878771
1.873817e-02 0.1653805 0.8799604 0.12357893
2.154435e-02 0.1729973 0.8717956 0.12971927
2.477076e-02 0.1824785 0.8603090 0.13741038
2.848036e-02 0.1928623 0.8473535 0.14581335
3.274549e-02 0.2039627 0.8334474 0.15485133
3.764936e-02 0.2162439 0.8174807 0.16493342
4.328761e-02 0.2298441 0.7988964 0.17609103
4.977024e-02 0.2463958 0.7711065 0.18966157
5.722368e-02 0.2665106 0.7278567 0.20621242
6.579332e-02 0.2890668 0.6679143 0.22482408
7.564633e-02 0.3108364 0.6023591 0.24281317
8.697490e-02 0.3269388 0.5605917 0.25621639
1.000000e-01 0.3417624 0.5270994 0.26858359
1.149757e-01 0.3565354 0.4957222 0.28104235
1.321941e-01 0.3710455 0.4761545 0.29352937
1.519911e-01 0.3872305 0.4578507 0.30762038
1.747528e-01 0.4061797 0.4320649 0.32419438
2.009233e-01 0.4274621 0.3153036 0.34240298
2.310130e-01 0.4411191 NaN 0.35246969
2.656088e-01 0.4411191 NaN 0.35246969
3.053856e-01 0.4411191 NaN 0.35246969
3.511192e-01 0.4411191 NaN 0.35246969
4.037017e-01 0.4411191 NaN 0.35246969
4.641589e-01 0.4411191 NaN 0.35246969
5.336699e-01 0.4411191 NaN 0.35246969
6.135907e-01 0.4411191 NaN 0.35246969
7.054802e-01 0.4411191 NaN 0.35246969
8.111308e-01 0.4411191 NaN 0.35246969
9.326033e-01 0.4411191 NaN 0.35246969
1.072267e+00 0.4411191 NaN 0.35246969
1.232847e+00 0.4411191 NaN 0.35246969
1.417474e+00 0.4411191 NaN 0.35246969
1.629751e+00 0.4411191 NaN 0.35246969
1.873817e+00 0.4411191 NaN 0.35246969
2.154435e+00 0.4411191 NaN 0.35246969
2.477076e+00 0.4411191 NaN 0.35246969
2.848036e+00 0.4411191 NaN 0.35246969
3.274549e+00 0.4411191 NaN 0.35246969
3.764936e+00 0.4411191 NaN 0.35246969
4.328761e+00 0.4411191 NaN 0.35246969
4.977024e+00 0.4411191 NaN 0.35246969
5.722368e+00 0.4411191 NaN 0.35246969
6.579332e+00 0.4411191 NaN 0.35246969
7.564633e+00 0.4411191 NaN 0.35246969
8.697490e+00 0.4411191 NaN 0.35246969
1.000000e+01 0.4411191 NaN 0.35246969
1.149757e+01 0.4411191 NaN 0.35246969
1.321941e+01 0.4411191 NaN 0.35246969
1.519911e+01 0.4411191 NaN 0.35246969
1.747528e+01 0.4411191 NaN 0.35246969
2.009233e+01 0.4411191 NaN 0.35246969
2.310130e+01 0.4411191 NaN 0.35246969
2.656088e+01 0.4411191 NaN 0.35246969
3.053856e+01 0.4411191 NaN 0.35246969
3.511192e+01 0.4411191 NaN 0.35246969
4.037017e+01 0.4411191 NaN 0.35246969
4.641589e+01 0.4411191 NaN 0.35246969
5.336699e+01 0.4411191 NaN 0.35246969
6.135907e+01 0.4411191 NaN 0.35246969
7.054802e+01 0.4411191 NaN 0.35246969
8.111308e+01 0.4411191 NaN 0.35246969
9.326033e+01 0.4411191 NaN 0.35246969
1.072267e+02 0.4411191 NaN 0.35246969
1.232847e+02 0.4411191 NaN 0.35246969
1.417474e+02 0.4411191 NaN 0.35246969
1.629751e+02 0.4411191 NaN 0.35246969
1.873817e+02 0.4411191 NaN 0.35246969
2.154435e+02 0.4411191 NaN 0.35246969
2.477076e+02 0.4411191 NaN 0.35246969
2.848036e+02 0.4411191 NaN 0.35246969
3.274549e+02 0.4411191 NaN 0.35246969
3.764936e+02 0.4411191 NaN 0.35246969
4.328761e+02 0.4411191 NaN 0.35246969
4.977024e+02 0.4411191 NaN 0.35246969
5.722368e+02 0.4411191 NaN 0.35246969
6.579332e+02 0.4411191 NaN 0.35246969
7.564633e+02 0.4411191 NaN 0.35246969
8.697490e+02 0.4411191 NaN 0.35246969
1.000000e+03 0.4411191 NaN 0.35246969
Tuning parameter 'alpha' was held constant at a value of 1
RMSE was used to select the optimal model using the smallest value.
The final values used for the model were alpha = 1 and lambda = 0.001.lassoLambda<-lassoModel$bestTune$lambda
lassoPredictor<- setdiff(names(carbonTrainData),"CarbonEmission")
lassoFinalModel<-glmnet(as.matrix(carbonTrainData[,lassoPredictor]),carbonTrainData[,"CarbonEmission"],alpha = 1,lambda = lassoLambda, family = "gaussian")Warning: NAs introduced by coercioncoeff<-coef(lassoFinalModel)
coeff29 x 1 sparse Matrix of class "dgCMatrix"
s0
(Intercept) 7.077965e+00
Body.Type .
Sex .
Diet .
How.Often.Shower .
Heating.Energy.Source .
Transport .
Vehicle.Type .
Social.Activity .
Monthly.Grocery.Bill 4.990372e-04
Frequency.of.Traveling.by.Air .
Vehicle.Monthly.Distance.Km 7.930046e-05
Waste.Bag.Size .
Waste.Bag.Weekly.Count 3.966627e-02
How.Long.TV.PC.Daily.Hour 6.828507e-04
How.Many.New.Clothes.Monthly 6.954544e-03
How.Long.Internet.Daily.Hour 3.466329e-03
Energy.efficiency .
Metal -6.157385e-02
Paper -7.169971e-02
Plastic -3.204086e-02
Glass -3.772139e-02
No_Recycling 1.536001e-02
Stove 7.934277e-03
Oven 2.805123e-02
Microwave 7.754147e-04
Grill 7.365362e-03
Airfryer 6.722616e-16
No_Cooking -1.394166e-02zeroCoeff<-coeff==0
zeroCoeff29 x 1 Matrix of class "lgeMatrix"
s0
(Intercept) FALSE
Body.Type TRUE
Sex TRUE
Diet TRUE
How.Often.Shower TRUE
Heating.Energy.Source TRUE
Transport TRUE
Vehicle.Type TRUE
Social.Activity TRUE
Monthly.Grocery.Bill FALSE
Frequency.of.Traveling.by.Air TRUE
Vehicle.Monthly.Distance.Km FALSE
Waste.Bag.Size TRUE
Waste.Bag.Weekly.Count FALSE
How.Long.TV.PC.Daily.Hour FALSE
How.Many.New.Clothes.Monthly FALSE
How.Long.Internet.Daily.Hour FALSE
Energy.efficiency TRUE
Metal FALSE
Paper FALSE
Plastic FALSE
Glass FALSE
No_Recycling FALSE
Stove FALSE
Oven FALSE
Microwave FALSE
Grill FALSE
Airfryer FALSE
No_Cooking FALSEplot(lassoModel)
#Ridge Model
set.seed(1)
ridgeModel<-train(CarbonEmission~.,data = carbonTrainData,method="glmnet",trControl= trainControl(method = "cv", number=5), tuneGrid = expand.grid(alpha=0, lambda=10^seq(-3,3,length=100))) Warning: There were missing values in resampled performance measures.ridgeModelglmnet
8001 samples
28 predictor
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 6400, 6402, 6400, 6401, 6401
Resampling results across tuning parameters:
lambda RMSE Rsquared MAE
1.000000e-03 0.1236296 0.9235380 0.08856450
1.149757e-03 0.1236296 0.9235380 0.08856450
1.321941e-03 0.1236296 0.9235380 0.08856450
1.519911e-03 0.1236296 0.9235380 0.08856450
1.747528e-03 0.1236296 0.9235380 0.08856450
2.009233e-03 0.1236296 0.9235380 0.08856450
2.310130e-03 0.1236296 0.9235380 0.08856450
2.656088e-03 0.1236296 0.9235380 0.08856450
3.053856e-03 0.1236296 0.9235380 0.08856450
3.511192e-03 0.1236296 0.9235380 0.08856450
4.037017e-03 0.1236296 0.9235380 0.08856450
4.641589e-03 0.1236296 0.9235380 0.08856450
5.336699e-03 0.1236296 0.9235380 0.08856450
6.135907e-03 0.1236296 0.9235380 0.08856450
7.054802e-03 0.1236296 0.9235380 0.08856450
8.111308e-03 0.1236296 0.9235380 0.08856450
9.326033e-03 0.1236296 0.9235380 0.08856450
1.072267e-02 0.1236296 0.9235380 0.08856450
1.232847e-02 0.1236296 0.9235380 0.08856450
1.417474e-02 0.1236296 0.9235380 0.08856450
1.629751e-02 0.1236296 0.9235380 0.08856450
1.873817e-02 0.1236296 0.9235380 0.08856450
2.154435e-02 0.1236296 0.9235380 0.08856450
2.477076e-02 0.1240623 0.9233222 0.08891353
2.848036e-02 0.1248547 0.9229254 0.08955337
3.274549e-02 0.1258460 0.9224294 0.09035832
3.764936e-02 0.1270691 0.9218201 0.09134409
4.328761e-02 0.1285767 0.9210687 0.09255793
4.977024e-02 0.1304045 0.9201612 0.09404563
5.722368e-02 0.1326181 0.9190609 0.09587905
6.579332e-02 0.1352510 0.9177554 0.09805509
7.564633e-02 0.1383809 0.9161965 0.10064929
8.697490e-02 0.1420236 0.9143856 0.10368213
1.000000e-01 0.1462655 0.9122644 0.10725472
1.149757e-01 0.1510922 0.9098562 0.11134292
1.321941e-01 0.1566022 0.9070774 0.11604694
1.519911e-01 0.1627365 0.9039836 0.12127662
1.747528e-01 0.1695983 0.9004756 0.12712566
2.009233e-01 0.1770753 0.8966475 0.13355774
2.310130e-01 0.1852805 0.8923696 0.14060318
2.656088e-01 0.1940398 0.8877770 0.14808518
3.053856e-01 0.2034777 0.8827064 0.15611211
3.511192e-01 0.2133536 0.8773463 0.16452594
4.037017e-01 0.2238071 0.8714935 0.17339893
4.641589e-01 0.2345317 0.8653986 0.18248118
5.336699e-01 0.2456875 0.8588186 0.19188157
6.135907e-01 0.2569113 0.8520765 0.20127888
7.054802e-01 0.2683892 0.8448914 0.21087395
8.111308e-01 0.2797186 0.8376721 0.22034450
9.326033e-01 0.2911145 0.8300828 0.22982984
1.072267e+00 0.3021559 0.8226070 0.23897247
1.232847e+00 0.3130907 0.8148905 0.24801865
1.417474e+00 0.3235011 0.8074583 0.25660788
1.629751e+00 0.3336608 0.7999214 0.26496175
1.873817e+00 0.3431761 0.7928249 0.27275909
2.154435e+00 0.3523368 0.7857526 0.28027521
2.477076e+00 0.3607877 0.7792359 0.28719687
2.848036e+00 0.3688233 0.7728451 0.29377210
3.274549e+00 0.3761349 0.7670706 0.29974973
3.764936e+00 0.3830101 0.7614879 0.30535400
4.328761e+00 0.3891895 0.7565288 0.31038294
4.977024e+00 0.3949431 0.7517928 0.31506421
5.722368e+00 0.4000593 0.7476450 0.31922348
6.579332e+00 0.4047823 0.7437226 0.32305636
7.564633e+00 0.4089439 0.7403284 0.32642816
8.697490e+00 0.4127580 0.7371445 0.32952218
1.000000e+01 0.4160930 0.7344155 0.33223076
1.149757e+01 0.4191313 0.7318718 0.33469680
1.321941e+01 0.4217713 0.7297079 0.33683915
1.519911e+01 0.4241648 0.7277011 0.33878063
1.747528e+01 0.4262338 0.7260039 0.34045671
2.009233e+01 0.4281024 0.7244361 0.34196880
2.310130e+01 0.4297111 0.7231162 0.34326913
2.656088e+01 0.4311594 0.7219006 0.34443845
3.053856e+01 0.4324024 0.7208809 0.34544178
3.511192e+01 0.4335188 0.7199439 0.34634248
4.037017e+01 0.4344745 0.7191601 0.34711333
4.641589e+01 0.4353311 0.7184411 0.34780407
5.336699e+01 0.4360631 0.7178408 0.34839424
6.135907e+01 0.4367183 0.7172914 0.34892252
7.054802e+01 0.4372773 0.7168332 0.34937326
8.111308e+01 0.4377771 0.7164137 0.34977623
9.326033e+01 0.4382030 0.7160645 0.35011968
1.072267e+02 0.4385835 0.7157452 0.35042640
1.232847e+02 0.4389075 0.7154796 0.35068750
1.417474e+02 0.4391967 0.7152369 0.35092063
1.629751e+02 0.4394428 0.7150352 0.35111896
1.873817e+02 0.4396624 0.7148510 0.35129595
2.154435e+02 0.4404229 0.7147417 0.35190859
2.477076e+02 0.4411191 NaN 0.35246969
2.848036e+02 0.4411191 NaN 0.35246969
3.274549e+02 0.4411191 NaN 0.35246969
3.764936e+02 0.4411191 NaN 0.35246969
4.328761e+02 0.4411191 NaN 0.35246969
4.977024e+02 0.4411191 NaN 0.35246969
5.722368e+02 0.4411191 NaN 0.35246969
6.579332e+02 0.4411191 NaN 0.35246969
7.564633e+02 0.4411191 NaN 0.35246969
8.697490e+02 0.4411191 NaN 0.35246969
1.000000e+03 0.4411191 NaN 0.35246969
Tuning parameter 'alpha' was held constant at a value of 0
RMSE was used to select the optimal model using the smallest value.
The final values used for the model were alpha = 0 and lambda = 0.02154435.ridgeLambda<-ridgeModel$bestTune$lambda
ridgePredictor<- setdiff(names(carbonTrainData),"CarbonEmission")
ridgeFinalModel<-glmnet(as.matrix(carbonTrainData[,ridgePredictor]),carbonTrainData[,"CarbonEmission"],alpha = 1,lambda = ridgeLambda, family = "gaussian")Warning: NAs introduced by coercionridgeFinalModel
Call: glmnet(x = as.matrix(carbonTrainData[, ridgePredictor]), y = carbonTrainData[, "CarbonEmission"], family = "gaussian", alpha = 1, lambda = ridgeLambda)
Df %Dev Lambda
1 8 35.92 0.02154plot(ridgeModel)
#Elastic Net Model
set.seed(1)
enetModel<-train(CarbonEmission~., data = carbonTrainData, method = "glmnet", trControl=trainControl(method="cv",number=5,preProc="nzv"),tuneGrid=expand.grid(alpha=seq(0,1,length=10),lambda=10^seq(-3,1,length=100)))Warning: There were missing values in resampled performance measures.enetModelglmnet
8001 samples
28 predictor
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 6400, 6402, 6400, 6401, 6401
Resampling results across tuning parameters:
alpha lambda RMSE Rsquared MAE
0.0000000 0.001000000 0.1236296 0.9235380 0.08856450
0.0000000 0.001097499 0.1236296 0.9235380 0.08856450
0.0000000 0.001204504 0.1236296 0.9235380 0.08856450
0.0000000 0.001321941 0.1236296 0.9235380 0.08856450
0.0000000 0.001450829 0.1236296 0.9235380 0.08856450
0.0000000 0.001592283 0.1236296 0.9235380 0.08856450
0.0000000 0.001747528 0.1236296 0.9235380 0.08856450
0.0000000 0.001917910 0.1236296 0.9235380 0.08856450
0.0000000 0.002104904 0.1236296 0.9235380 0.08856450
0.0000000 0.002310130 0.1236296 0.9235380 0.08856450
0.0000000 0.002535364 0.1236296 0.9235380 0.08856450
0.0000000 0.002782559 0.1236296 0.9235380 0.08856450
0.0000000 0.003053856 0.1236296 0.9235380 0.08856450
0.0000000 0.003351603 0.1236296 0.9235380 0.08856450
0.0000000 0.003678380 0.1236296 0.9235380 0.08856450
0.0000000 0.004037017 0.1236296 0.9235380 0.08856450
0.0000000 0.004430621 0.1236296 0.9235380 0.08856450
0.0000000 0.004862602 0.1236296 0.9235380 0.08856450
0.0000000 0.005336699 0.1236296 0.9235380 0.08856450
0.0000000 0.005857021 0.1236296 0.9235380 0.08856450
0.0000000 0.006428073 0.1236296 0.9235380 0.08856450
0.0000000 0.007054802 0.1236296 0.9235380 0.08856450
0.0000000 0.007742637 0.1236296 0.9235380 0.08856450
0.0000000 0.008497534 0.1236296 0.9235380 0.08856450
0.0000000 0.009326033 0.1236296 0.9235380 0.08856450
0.0000000 0.010235310 0.1236296 0.9235380 0.08856450
0.0000000 0.011233240 0.1236296 0.9235380 0.08856450
0.0000000 0.012328467 0.1236296 0.9235380 0.08856450
0.0000000 0.013530478 0.1236296 0.9235380 0.08856450
0.0000000 0.014849683 0.1236296 0.9235380 0.08856450
0.0000000 0.016297508 0.1236296 0.9235380 0.08856450
0.0000000 0.017886495 0.1236296 0.9235380 0.08856450
0.0000000 0.019630407 0.1236296 0.9235380 0.08856450
0.0000000 0.021544347 0.1236296 0.9235380 0.08856450
0.0000000 0.023644894 0.1238344 0.9234369 0.08873071
0.0000000 0.025950242 0.1243062 0.9232001 0.08910784
0.0000000 0.028480359 0.1248547 0.9229254 0.08955337
0.0000000 0.031257158 0.1254902 0.9226076 0.09007142
0.0000000 0.034304693 0.1262242 0.9222412 0.09066357
0.0000000 0.037649358 0.1270691 0.9218201 0.09134409
0.0000000 0.041320124 0.1280380 0.9213377 0.09212398
0.0000000 0.045348785 0.1291449 0.9207872 0.09301807
0.0000000 0.049770236 0.1304045 0.9201612 0.09404563
0.0000000 0.054622772 0.1318313 0.9194531 0.09522700
0.0000000 0.059948425 0.1334415 0.9186540 0.09656021
0.0000000 0.065793322 0.1352510 0.9177554 0.09805509
0.0000000 0.072208090 0.1372745 0.9167500 0.09973249
0.0000000 0.079248290 0.1395276 0.9156288 0.10160035
0.0000000 0.086974900 0.1420236 0.9143856 0.10368213
0.0000000 0.095454846 0.1447753 0.9130136 0.10599583
0.0000000 0.104761575 0.1477942 0.9115069 0.10854740
0.0000000 0.114975700 0.1510922 0.9098562 0.11134292
0.0000000 0.126185688 0.1546780 0.9080552 0.11439741
0.0000000 0.138488637 0.1585579 0.9060991 0.11771529
0.0000000 0.151991108 0.1627365 0.9039836 0.12127662
0.0000000 0.166810054 0.1672161 0.9017050 0.12509205
0.0000000 0.183073828 0.1719965 0.8992602 0.12918270
0.0000000 0.200923300 0.1770753 0.8966475 0.13355774
0.0000000 0.220513074 0.1824476 0.8938641 0.13816952
0.0000000 0.242012826 0.1881059 0.8909074 0.14302527
0.0000000 0.265608778 0.1940398 0.8877770 0.14808518
0.0000000 0.291505306 0.2002365 0.8844729 0.15335458
0.0000000 0.319926714 0.2066805 0.8809955 0.15884261
0.0000000 0.351119173 0.2133536 0.8773463 0.16452594
0.0000000 0.385352859 0.2202354 0.8735277 0.17037196
0.0000000 0.422924287 0.2273031 0.8695434 0.17636285
0.0000000 0.464158883 0.2345317 0.8653986 0.18248118
0.0000000 0.509413801 0.2418949 0.8610998 0.18869095
0.0000000 0.559081018 0.2493644 0.8566556 0.19496905
0.0000000 0.613590727 0.2569113 0.8520765 0.20127888
0.0000000 0.673415066 0.2645058 0.8473748 0.20762461
0.0000000 0.739072203 0.2721184 0.8425692 0.21399504
0.0000000 0.811130831 0.2797186 0.8376721 0.22034450
0.0000000 0.890215085 0.2872768 0.8326965 0.22664091
0.0000000 0.977009957 0.2947649 0.8276676 0.23285533
0.0000000 1.072267222 0.3021559 0.8226070 0.23897247
0.0000000 1.176811952 0.3094244 0.8175368 0.24498596
0.0000000 1.291549665 0.3165466 0.8124797 0.25087384
0.0000000 1.417474163 0.3235011 0.8074583 0.25660788
0.0000000 1.555676144 0.3302684 0.8024947 0.26217619
0.0000000 1.707352647 0.3368316 0.7976103 0.26756072
0.0000000 1.873817423 0.3431761 0.7928249 0.27275909
0.0000000 2.056512308 0.3492896 0.7881569 0.27777537
0.0000000 2.257019720 0.3551626 0.7836225 0.28259195
0.0000000 2.477076356 0.3607877 0.7792359 0.28719687
0.0000000 2.718588243 0.3661597 0.7750090 0.29159314
0.0000000 2.983647240 0.3712757 0.7709515 0.29577793
0.0000000 3.274549163 0.3761349 0.7670706 0.29974973
0.0000000 3.593813664 0.3807383 0.7633715 0.30350446
0.0000000 3.944206059 0.3850884 0.7598572 0.30704491
0.0000000 4.328761281 0.3891895 0.7565288 0.31038294
0.0000000 4.750810162 0.3930472 0.7533859 0.31352223
0.0000000 5.214008288 0.3966679 0.7504259 0.31646616
0.0000000 5.722367659 0.4000593 0.7476450 0.31922348
0.0000000 6.280291442 0.4032297 0.7450389 0.32179736
0.0000000 6.892612104 0.4061881 0.7426021 0.32419540
0.0000000 7.564633276 0.4089439 0.7403284 0.32642816
0.0000000 8.302175681 0.4115067 0.7382109 0.32850613
0.0000000 9.111627561 0.4138865 0.7362424 0.33043876
0.0000000 10.000000000 0.4160930 0.7344155 0.33223076
0.1111111 0.001000000 0.1212244 0.9247599 0.08667484
0.1111111 0.001097499 0.1212244 0.9247599 0.08667484
0.1111111 0.001204504 0.1212244 0.9247599 0.08667484
0.1111111 0.001321941 0.1212244 0.9247599 0.08667484
0.1111111 0.001450829 0.1212253 0.9247594 0.08667531
0.1111111 0.001592283 0.1212316 0.9247551 0.08667904
0.1111111 0.001747528 0.1212391 0.9247500 0.08668362
0.1111111 0.001917910 0.1212477 0.9247444 0.08668905
0.1111111 0.002104904 0.1212576 0.9247380 0.08669519
0.1111111 0.002310130 0.1212708 0.9247287 0.08670357
0.1111111 0.002535364 0.1212851 0.9247200 0.08671278
0.1111111 0.002782559 0.1213038 0.9247078 0.08672566
0.1111111 0.003053856 0.1213264 0.9246932 0.08674138
0.1111111 0.003351603 0.1213535 0.9246757 0.08676010
0.1111111 0.003678380 0.1213861 0.9246547 0.08678325
0.1111111 0.004037017 0.1214253 0.9246296 0.08681115
0.1111111 0.004430621 0.1214723 0.9245995 0.08684541
0.1111111 0.004862602 0.1215285 0.9245636 0.08688706
0.1111111 0.005336699 0.1215955 0.9245211 0.08693708
0.1111111 0.005857021 0.1216734 0.9244725 0.08699575
0.1111111 0.006428073 0.1217640 0.9244167 0.08706176
0.1111111 0.007054802 0.1218723 0.9243517 0.08714020
0.1111111 0.007742637 0.1220023 0.9242730 0.08723349
0.1111111 0.008497534 0.1221574 0.9241789 0.08734784
0.1111111 0.009326033 0.1223418 0.9240671 0.08748788
0.1111111 0.010235310 0.1225610 0.9239338 0.08765828
0.1111111 0.011233240 0.1228206 0.9237757 0.08786128
0.1111111 0.012328467 0.1231251 0.9235903 0.08810148
0.1111111 0.013530478 0.1234858 0.9233706 0.08839310
0.1111111 0.014849683 0.1239090 0.9231112 0.08873575
0.1111111 0.016297508 0.1244137 0.9227994 0.08913617
0.1111111 0.017886495 0.1250059 0.9224297 0.08961301
0.1111111 0.019630407 0.1256982 0.9219954 0.09017614
0.1111111 0.021544347 0.1265143 0.9214749 0.09085306
0.1111111 0.023644894 0.1274679 0.9208584 0.09165533
0.1111111 0.025950242 0.1285776 0.9201331 0.09258548
0.1111111 0.028480359 0.1298571 0.9192845 0.09366354
0.1111111 0.031257158 0.1313419 0.9182803 0.09491783
0.1111111 0.034304693 0.1330534 0.9170988 0.09637225
0.1111111 0.037649358 0.1350118 0.9157160 0.09803835
0.1111111 0.041320124 0.1372378 0.9141128 0.09990429
0.1111111 0.045348785 0.1397448 0.9122722 0.10199035
0.1111111 0.049770236 0.1425567 0.9101670 0.10431658
0.1111111 0.054622772 0.1456757 0.9077977 0.10690385
0.1111111 0.059948425 0.1490664 0.9052449 0.10972602
0.1111111 0.065793322 0.1526953 0.9025983 0.11274363
0.1111111 0.072208090 0.1565559 0.8999013 0.11596297
0.1111111 0.079248290 0.1606923 0.8971276 0.11941841
0.1111111 0.086974900 0.1652388 0.8939947 0.12322742
0.1111111 0.095454846 0.1702413 0.8904195 0.12746901
0.1111111 0.104761575 0.1756154 0.8865688 0.13200873
0.1111111 0.114975700 0.1812278 0.8828346 0.13672245
0.1111111 0.126185688 0.1872798 0.8787716 0.14179500
0.1111111 0.138488637 0.1937847 0.8743191 0.14724908
0.1111111 0.151991108 0.2007984 0.8692493 0.15313792
0.1111111 0.166810054 0.2083792 0.8633033 0.15946987
0.1111111 0.183073828 0.2165454 0.8562948 0.16629903
0.1111111 0.200923300 0.2253000 0.8479675 0.17364441
0.1111111 0.220513074 0.2344633 0.8387523 0.18128129
0.1111111 0.242012826 0.2438032 0.8294846 0.18904129
0.1111111 0.265608778 0.2535871 0.8188895 0.19715695
0.1111111 0.291505306 0.2636993 0.8071145 0.20551204
0.1111111 0.319926714 0.2741153 0.7940282 0.21409987
0.1111111 0.351119173 0.2847082 0.7799562 0.22286315
0.1111111 0.385352859 0.2954869 0.7645372 0.23179262
0.1111111 0.422924287 0.3066095 0.7461381 0.24098325
0.1111111 0.464158883 0.3181258 0.7231542 0.25046063
0.1111111 0.509413801 0.3299815 0.6942349 0.26020522
0.1111111 0.559081018 0.3419844 0.6587992 0.27006763
0.1111111 0.613590727 0.3534831 0.6214239 0.27955448
0.1111111 0.673415066 0.3639929 0.5889376 0.28828655
0.1111111 0.739072203 0.3733378 0.5641600 0.29609432
0.1111111 0.811130831 0.3815679 0.5498502 0.30290536
0.1111111 0.890215085 0.3896798 0.5334248 0.30961589
0.1111111 0.977009957 0.3976221 0.5136393 0.31618662
0.1111111 1.072267222 0.4048771 0.5025919 0.32221816
0.1111111 1.176811952 0.4119484 0.4881507 0.32815474
0.1111111 1.291549665 0.4186307 0.4710747 0.33383641
0.1111111 1.417474163 0.4250836 0.4411928 0.33933744
0.1111111 1.555676144 0.4305980 0.4235564 0.34401198
0.1111111 1.707352647 0.4357652 0.3723274 0.34836787
0.1111111 1.873817423 0.4392024 0.2635578 0.35113247
0.1111111 2.056512308 0.4411105 0.2443734 0.35246376
0.1111111 2.257019720 0.4411191 NaN 0.35246969
0.1111111 2.477076356 0.4411191 NaN 0.35246969
0.1111111 2.718588243 0.4411191 NaN 0.35246969
0.1111111 2.983647240 0.4411191 NaN 0.35246969
0.1111111 3.274549163 0.4411191 NaN 0.35246969
0.1111111 3.593813664 0.4411191 NaN 0.35246969
0.1111111 3.944206059 0.4411191 NaN 0.35246969
0.1111111 4.328761281 0.4411191 NaN 0.35246969
0.1111111 4.750810162 0.4411191 NaN 0.35246969
0.1111111 5.214008288 0.4411191 NaN 0.35246969
0.1111111 5.722367659 0.4411191 NaN 0.35246969
0.1111111 6.280291442 0.4411191 NaN 0.35246969
0.1111111 6.892612104 0.4411191 NaN 0.35246969
0.1111111 7.564633276 0.4411191 NaN 0.35246969
0.1111111 8.302175681 0.4411191 NaN 0.35246969
0.1111111 9.111627561 0.4411191 NaN 0.35246969
0.1111111 10.000000000 0.4411191 NaN 0.35246969
[ reached getOption("max.print") -- omitted 800 rows ]
RMSE was used to select the optimal model using the smallest value.
The final values used for the model were alpha = 0.1111111 and lambda = 0.001321941.enetModel$bestTuneenetLambda<-enetModel$bestTune$lambda
enetAlpha<-enetModel$bestTune$alpha
enetPredector<-setdiff(names(carbonTrainData),"CarbonEmission")
enetFinalModel<-glmnet(as.matrix(carbonTrainData[,enetPredector]),carbonTrainData[,"CarbonEmission"], alpha = enetAlpha,lambda = enetLambda, family = "gaussian")Warning: NAs introduced by coercionenetFinalModel
Call: glmnet(x = as.matrix(carbonTrainData[, enetPredector]), y = carbonTrainData[, "CarbonEmission"], family = "gaussian", alpha = enetAlpha, lambda = enetLambda)
Df %Dev Lambda
1 17 38.08 0.001322carbonTrainData$`No Recycling`NULL#Random Forest Model
library(randomForest)
set.seed(1)
randomForestModel<-randomForest(CarbonEmission~.,data = carbonTrainData)
randomForestModel
Call:
randomForest(formula = CarbonEmission ~ ., data = carbonTrainData)
Type of random forest: regression
Number of trees: 500
No. of variables tried at each split: 9
Mean of squared residuals: 0.01610385
% Var explained: 91.72mRf<-train(CarbonEmission~.,
data=carbonTrainData,
method="rf",
trControl=trainControl(method = "cv", number =5)
)mRfRandom Forest
8001 samples
28 predictor
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 6401, 6401, 6401, 6400, 6401
Resampling results across tuning parameters:
mtry RMSE Rsquared MAE
2 0.2712051 0.8060857 0.2121779
24 0.1382189 0.9061441 0.1065777
47 0.1427589 0.8970490 0.1106657
RMSE was used to select the optimal model using the smallest value.
The final value used for the model was mtry = 24.varImp(mRf)rf variable importance
only 20 most important variables shown (out of 47)rfPred<-predict(mRf,newdata = carbonTestData)
MAE(carbonTestData$CarbonEmission,rfPred)[1] 0.1042843rmse(carbonTestData$CarbonEmission,rfPred)[1] 0.005051824cor(carbonTestData$CarbonEmission,rfPred)^2[1] 0.907299plot(carbonTestData$CarbonEmission,rfPred)
GBM
set.seed(1)
grBoostedTree<-train(
CarbonEmission~.,
data = carbonTrainData,
method="gbm",
trControl=trainControl(method = "cv",number = 5)
)Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1863 nan 0.1000 0.0085
2 0.1789 nan 0.1000 0.0070
3 0.1715 nan 0.1000 0.0073
4 0.1654 nan 0.1000 0.0060
5 0.1595 nan 0.1000 0.0055
6 0.1550 nan 0.1000 0.0042
7 0.1503 nan 0.1000 0.0048
8 0.1462 nan 0.1000 0.0039
9 0.1424 nan 0.1000 0.0036
10 0.1383 nan 0.1000 0.0041
20 0.1140 nan 0.1000 0.0019
40 0.0886 nan 0.1000 0.0009
60 0.0720 nan 0.1000 0.0006
80 0.0600 nan 0.1000 0.0005
100 0.0513 nan 0.1000 0.0003
120 0.0449 nan 0.1000 0.0002
140 0.0399 nan 0.1000 0.0002
150 0.0379 nan 0.1000 0.0001
Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1795 nan 0.1000 0.0151
2 0.1671 nan 0.1000 0.0126
3 0.1571 nan 0.1000 0.0100
4 0.1486 nan 0.1000 0.0084
5 0.1413 nan 0.1000 0.0076
6 0.1348 nan 0.1000 0.0065
7 0.1286 nan 0.1000 0.0061
8 0.1230 nan 0.1000 0.0053
9 0.1183 nan 0.1000 0.0046
10 0.1136 nan 0.1000 0.0045
20 0.0847 nan 0.1000 0.0020
40 0.0562 nan 0.1000 0.0010
60 0.0409 nan 0.1000 0.0005
80 0.0320 nan 0.1000 0.0003
100 0.0261 nan 0.1000 0.0002
120 0.0222 nan 0.1000 0.0001
140 0.0192 nan 0.1000 0.0001
150 0.0180 nan 0.1000 0.0001
Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1759 nan 0.1000 0.0185
2 0.1607 nan 0.1000 0.0156
3 0.1479 nan 0.1000 0.0123
4 0.1379 nan 0.1000 0.0099
5 0.1281 nan 0.1000 0.0096
6 0.1206 nan 0.1000 0.0076
7 0.1141 nan 0.1000 0.0063
8 0.1084 nan 0.1000 0.0055
9 0.1028 nan 0.1000 0.0054
10 0.0983 nan 0.1000 0.0045
20 0.0700 nan 0.1000 0.0019
40 0.0422 nan 0.1000 0.0010
60 0.0296 nan 0.1000 0.0004
80 0.0224 nan 0.1000 0.0003
100 0.0177 nan 0.1000 0.0001
120 0.0147 nan 0.1000 0.0001
140 0.0123 nan 0.1000 0.0001
150 0.0113 nan 0.1000 0.0001
Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1868 nan 0.1000 0.0088
2 0.1792 nan 0.1000 0.0074
3 0.1718 nan 0.1000 0.0074
4 0.1659 nan 0.1000 0.0061
5 0.1597 nan 0.1000 0.0057
6 0.1548 nan 0.1000 0.0048
7 0.1498 nan 0.1000 0.0047
8 0.1457 nan 0.1000 0.0037
9 0.1416 nan 0.1000 0.0040
10 0.1381 nan 0.1000 0.0034
20 0.1136 nan 0.1000 0.0017
40 0.0888 nan 0.1000 0.0010
60 0.0722 nan 0.1000 0.0008
80 0.0605 nan 0.1000 0.0004
100 0.0517 nan 0.1000 0.0003
120 0.0453 nan 0.1000 0.0003
140 0.0404 nan 0.1000 0.0002
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Iter TrainDeviance ValidDeviance StepSize Improve
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Iter TrainDeviance ValidDeviance StepSize Improve
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2 0.1655 nan 0.1000 0.0128
3 0.1553 nan 0.1000 0.0102
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Iter TrainDeviance ValidDeviance StepSize Improve
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4 0.1366 nan 0.1000 0.0104
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Iter TrainDeviance ValidDeviance StepSize Improve
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2 0.1782 nan 0.1000 0.0075
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Iter TrainDeviance ValidDeviance StepSize Improve
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Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1756 nan 0.1000 0.0190
2 0.1605 nan 0.1000 0.0152
3 0.1477 nan 0.1000 0.0125
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Iter TrainDeviance ValidDeviance StepSize Improve
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2 0.1767 nan 0.1000 0.0075
3 0.1697 nan 0.1000 0.0071
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Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1783 nan 0.1000 0.0152
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3 0.1558 nan 0.1000 0.0101
4 0.1472 nan 0.1000 0.0084
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Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1748 nan 0.1000 0.0189
2 0.1592 nan 0.1000 0.0155
3 0.1466 nan 0.1000 0.0128
4 0.1365 nan 0.1000 0.0101
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Iter TrainDeviance ValidDeviance StepSize Improve
1 0.1753 nan 0.1000 0.0188
2 0.1599 nan 0.1000 0.0153
3 0.1472 nan 0.1000 0.0125
4 0.1372 nan 0.1000 0.0100
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140 0.0125 nan 0.1000 0.0000
150 0.0116 nan 0.1000 0.0001grBoostedTreeStochastic Gradient Boosting
8001 samples
28 predictor
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 6400, 6402, 6400, 6401, 6401
Resampling results across tuning parameters:
interaction.depth n.trees RMSE Rsquared MAE
1 50 0.2825681 0.6848913 0.21910129
1 100 0.2276478 0.7963534 0.17429897
1 150 0.1965925 0.8385292 0.14854439
2 50 0.2193290 0.8179728 0.16722911
2 100 0.1644147 0.8876415 0.12355000
2 150 0.1375675 0.9160870 0.10256446
3 50 0.1897724 0.8600842 0.14451760
3 100 0.1377020 0.9190183 0.10349530
3 150 0.1115734 0.9430885 0.08264941
Tuning parameter 'shrinkage' was held constant at a value of 0.1
Tuning parameter 'n.minobsinnode' was held constant at a value of 10
RMSE was used to select the optimal model using the smallest value.
The final values used for the model were n.trees = 150, interaction.depth = 3, shrinkage = 0.1 and n.minobsinnode = 10.gbmPred<-predict(grBoostedTree, carbonTestData)MAE(carbonTestData$CarbonEmission,gbmPred)[1] 0.08071192rmse(carbonTestData$CarbonEmission,gbmPred)[1] 0.0006674298cor(carbonTestData$CarbonEmission,gbmPred)^2[1] 0.9428016plot(carbonTestData$CarbonEmission,gbmPred)
#SV Linear Model
set.seed(1)
svmLinear<-train(
CarbonEmission~.,
data = carbonTrainData,
method="svmLinear",
trControl=trainControl(method = "cv",number = 5, preProc=c("center","scale"))
)svmLinearSupport Vector Machines with Linear Kernel
8001 samples
28 predictor
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 6400, 6402, 6400, 6401, 6401
Resampling results:
RMSE Rsquared MAE
0.1216608 0.9244839 0.08585286
Tuning parameter 'C' was held constant at a value of 1svmPred<-predict(svmLinear,carbonTestData)
plot(svmPred,carbonTestData$CarbonEmission)
#SVM Radial Model
set.seed(1)
svmRadial<-train(
CarbonEmission~.,
data = carbonTrainData,
method="svmRadial",
trControl=trainControl(method = "cv",number = 5, preProc=c("center","scale"))
)svmRadialSupport Vector Machines with Radial Basis Function Kernel
8001 samples
28 predictor
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 6400, 6402, 6400, 6401, 6401
Resampling results across tuning parameters:
C RMSE Rsquared MAE
0.25 0.08190788 0.9678058 0.05674104
0.50 0.06914040 0.9764828 0.04865093
1.00 0.06154311 0.9809844 0.04422410
Tuning parameter 'sigma' was held constant at a value of 0.01148182
RMSE was used to select the optimal model using the smallest value.
The final values used for the model were sigma = 0.01148182 and C = 1.svmRadialPred<-predict(svmRadial,carbonTestData)
plot(svmRadialPred,carbonTestData$CarbonEmission)
#Comparing models
compare=resamples(list(KNN=knnModel,LIN=lmModel,stepWise=stepwiseModel,Lasso=lassoModel,Ridge=ridgeModel,Enet=enetModel,RF=mRf,GBM=grBoostedTree,SVML=svmLinear,SVMR=svmRadial))
summary(compare) # Out of all the models SVM Radial stands out the most
Call:
summary.resamples(object = compare)
Models: KNN, LIN, stepWise, Lasso, Ridge, Enet, RF, GBM, SVML, SVMR
Number of resamples: 5
MAE
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
KNN 0.29623471 0.29740921 0.30499011 0.30249251 0.30575296 0.30807555 0
LIN 0.08480145 0.08612879 0.08704453 0.08667648 0.08732807 0.08807956 0
stepWise 0.27060240 0.27186835 0.27300079 0.27312929 0.27490680 0.27526812 0
Lasso 0.08496466 0.08611094 0.08720014 0.08687629 0.08723989 0.08886584 0
Ridge 0.08691866 0.08723220 0.08858797 0.08856450 0.08950638 0.09057731 0
Enet 0.08445732 0.08608927 0.08684209 0.08667484 0.08711682 0.08886872 0
RF 0.10394857 0.10611419 0.10628345 0.10657774 0.10707089 0.10947160 0
GBM 0.08008938 0.08165398 0.08346939 0.08264941 0.08390377 0.08413051 0
SVML 0.08366201 0.08516816 0.08566449 0.08585286 0.08648885 0.08828078 0
SVMR 0.04362895 0.04386712 0.04451333 0.04422410 0.04452306 0.04458802 0
RMSE
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
KNN 0.37137353 0.38041941 0.38481702 0.38142506 0.38485362 0.38566170 0
LIN 0.11859265 0.12133365 0.12142487 0.12108930 0.12164347 0.12245187 0
stepWise 0.33950584 0.34519992 0.34662453 0.34713775 0.35174536 0.35261310 0
Lasso 0.11828998 0.12011679 0.12300251 0.12149735 0.12301315 0.12306432 0
Ridge 0.11999485 0.12265571 0.12476610 0.12362958 0.12481897 0.12591227 0
Enet 0.11826611 0.11953850 0.12259232 0.12122440 0.12285103 0.12287402 0
RF 0.13240909 0.13682774 0.13761090 0.13821888 0.14192035 0.14232634 0
GBM 0.10729648 0.11057936 0.11283863 0.11157342 0.11325280 0.11389984 0
SVML 0.11867684 0.11977993 0.12295302 0.12166077 0.12340309 0.12349097 0
SVMR 0.06067301 0.06135216 0.06154531 0.06154311 0.06180787 0.06233721 0
Rsquared
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
KNN 0.2445364 0.2467487 0.2682413 0.2635529 0.2744761 0.2837618 0
LIN 0.9185288 0.9241334 0.9249591 0.9245749 0.9264152 0.9288379 0
stepWise 0.3578135 0.3697142 0.3798824 0.3809492 0.3880434 0.4092925 0
Lasso 0.9224198 0.9225172 0.9253228 0.9245000 0.9257084 0.9265317 0
Ridge 0.9216849 0.9217637 0.9243347 0.9235380 0.9248964 0.9250104 0
Enet 0.9225978 0.9226924 0.9254929 0.9247599 0.9258395 0.9271770 0
RF 0.9001725 0.9046329 0.9068228 0.9061441 0.9088380 0.9102542 0
GBM 0.9407131 0.9426509 0.9428430 0.9430885 0.9438202 0.9454152 0
SVML 0.9220482 0.9224530 0.9249043 0.9244839 0.9254125 0.9276015 0
SVMR 0.9805677 0.9807548 0.9809038 0.9809844 0.9810939 0.9816018 0Neural Network Preprocessing
library(caret)
carbonInd<-createDataPartition(carbonTrainData$CarbonEmission,p=0.9,list = FALSE)
carbonIndex<-which(names(carbonTrainData)=='CarbonEmission')
carbonTrainingData<-carbonTrainData[carbonInd,-carbonIndex]
str(carbonTrainingData)'data.frame': 7202 obs. of 28 variables:
$ Body.Type : Factor w/ 4 levels "normal","obese",..: 3 2 3 2 3 4 4 3 4 1 ...
$ Sex : Factor w/ 2 levels "female","male": 1 1 2 1 2 1 1 2 1 1 ...
$ Diet : Factor w/ 4 levels "omnivore","pescatarian",..: 2 4 1 4 4 3 3 1 2 4 ...
$ How.Often.Shower : Factor w/ 4 levels "daily","less frequently",..: 1 2 4 1 2 2 3 1 1 3 ...
$ Heating.Energy.Source : Factor w/ 4 levels "coal","electricity",..: 1 3 4 1 4 4 1 4 4 4 ...
$ Transport : Factor w/ 3 levels "private","public",..: 2 3 3 1 2 1 3 2 2 2 ...
$ Vehicle.Type : Factor w/ 6 levels "diesel","electric",..: 3 3 3 1 3 4 3 3 3 3 ...
$ Social.Activity : Factor w/ 3 levels "never","often",..: 2 2 3 2 3 1 3 1 2 1 ...
$ Monthly.Grocery.Bill : num 230 114 157 266 144 56 59 200 135 146 ...
$ Frequency.of.Traveling.by.Air: Factor w/ 4 levels "frequently","never",..: 1 3 3 4 1 3 4 1 3 2 ...
$ Vehicle.Monthly.Distance.Km : num 210 9 74 8457 658 ...
$ Waste.Bag.Size : Factor w/ 4 levels "extra large",..: 2 1 3 2 2 3 1 3 1 1 ...
$ Waste.Bag.Weekly.Count : num 4 3 3 1 1 4 3 3 1 4 ...
$ How.Long.TV.PC.Daily.Hour : num 7 9 20 3 22 9 5 3 8 12 ...
$ How.Many.New.Clothes.Monthly : num 26 38 5 5 18 11 39 31 23 27 ...
$ How.Long.Internet.Daily.Hour : num 1 5 7 6 9 19 15 15 18 21 ...
$ Energy.efficiency : Factor w/ 3 levels "No","Sometimes",..: 1 1 2 3 2 2 1 3 2 1 ...
$ Metal : num 1 1 1 0 1 0 0 0 0 0 ...
$ Paper : num 0 0 1 1 1 0 1 0 0 1 ...
$ Plastic : num 0 0 1 0 0 0 1 0 0 1 ...
$ Glass : num 0 0 1 0 1 0 1 1 1 0 ...
$ No_Recycling : num 0 0 0 0 0 1 0 0 0 0 ...
$ Stove : num 1 1 0 0 1 0 1 0 0 1 ...
$ Oven : num 1 0 0 1 1 0 0 0 0 0 ...
$ Microwave : num 0 1 1 0 1 0 1 1 1 1 ...
$ Grill : num 0 0 1 0 0 1 0 1 1 0 ...
$ Airfryer : num 0 0 1 0 0 1 0 1 1 0 ...
$ No_Cooking : num 0 0 0 0 0 0 0 0 0 0 ...carbonTrainingLabels<-carbonTrainData[carbonInd,carbonIndex]
str(carbonTrainingLabels) num [1:7202] 7.71 7.55 6.98 8.46 7.41 ...carbonValidationData<-carbonTrainData[-carbonInd,-carbonIndex]
carbonValidationData
carbonValidationLabels<-carbonTrainData[-carbonInd,carbonIndex]
str(carbonValidationLabels) num [1:799] 6.93 7.5 7.41 7.51 7.13 ...carbonTestingData<-carbonTestData[,-carbonIndex]
carbonTestingData
carbonTestingLabels<-carbonTestData[,carbonIndex]
str(carbonTestingLabels) num [1:1999] 7.86 6.56 7.6 7.35 8.27 ...dim(carbonTrainingData)[1] 7202 28dim(carbonTestingData)[1] 1999 28#Scaling numeric Variables and one hot encoding categorical variables
library(mltools)
library(data.table)
numericCols<-c("Monthly.Grocery.Bill","Vehicle.Monthly.Distance.Km","Waste.Bag.Weekly.Count",
"How.Long.TV.PC.Daily.Hour","How.Many.New.Clothes.Monthly","How.Long.Internet.Daily.Hour","Metal","Paper","Plastic","Glass","Stove","Oven"
,"Microwave","Grill","Airfryer","No_Cooking","No_Recycling")
categoricalCols<-c("Body.Type","Sex","Diet","How.Often.Shower","Heating.Energy.Source","Transport","Vehicle.Type","Social.Activity",
"Frequency.of.Traveling.by.Air","Waste.Bag.Size","Energy.efficiency")
carbonTrainingDataNew<-scale(carbonTrainingData[,numericCols])
colMeanTrain<-attr(carbonTrainingDataNew,"scaled:center")
colStddevsTrain<-attr(carbonTrainingDataNew,"scaled:scale")
carbonTrainingData[,numericCols]<-carbonTrainingDataNew
carbonValidationData[,numericCols]<-scale(carbonValidationData[,numericCols],center = colMeanTrain,scale = colStddevsTrain)
carbonTestingData[,numericCols]<-scale(carbonTestingData[,numericCols],center = colMeanTrain,scale = colStddevsTrain)
carbonTrainingTable<-as.data.table(carbonTrainingData)
carbonValidationTable<-as.data.table(carbonValidationData)
carbonTestingTable<-as.data.table(carbonTestingData)
carbonTrainingOneHot<-one_hot(carbonTrainingTable,naCols=FALSE,dropCols=TRUE,dropUnusedLevels=TRUE)
carbonTrainingOneHot
carbonValidationOneHot<-one_hot(carbonValidationTable,naCols=FALSE,dropCols=TRUE,dropUnusedLevels=TRUE)
carbonValidationOneHot
carbonTestingOneHot<-one_hot(carbonTestingTable,naCols=FALSE,dropCols=TRUE,dropUnusedLevels=TRUE)
carbonTestingOneHot
carbonTrainingFinal<-as.data.frame(cbind(carbonTrainingTable[, ..numericCols], carbonTrainingOneHot))
carbonTrainingFinal
carbonValidationFinal<-as.data.frame(cbind(carbonValidationTable[, ..numericCols], carbonValidationOneHot))
carbonValidationFinal
carbonTestingFinal<-as.data.frame(cbind(carbonTestingTable[, ..numericCols], carbonTestingOneHot))
carbonTestingFinallibrary(keras)
model<-keras_model_sequential()%>%
layer_dense(units = 32,activation = "relu",input_shape = dim(carbonTrainingFinal)[2])%>%
layer_dropout(rate=0.3)%>%
layer_dense(units = 32,activation = "relu")%>%
layer_dropout(rate=0.3)%>%
layer_dense(units = 16,activation = "relu")%>%
layer_dropout(rate=0.3)%>%
layer_dense(units = 1)/Users/angadsingh/.virtualenvs/r-tensorflow/lib/python3.9/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020
warnings.warn(
2024-05-06 19:15:32.845040: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M2 Pro
2024-05-06 19:15:32.845063: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 16.00 GB
2024-05-06 19:15:32.845070: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 5.33 GB
2024-05-06 19:15:32.845150: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:303] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2024-05-06 19:15:32.845446: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:269] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)model %>% compile(
loss="mse",
optimizer=optimizer_adam(lr=0.001)
)WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.history<-model %>% fit(as.matrix(carbonTrainingFinal),
carbonTrainingLabels,
batch_size=50,
epochs=20,
validation_data=list(as.matrix(carbonValidationFinal),carbonValidationLabels)
)Epoch 1/202024-05-06 19:15:34.205221: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
1/145 [..............................] - ETA: 6:28 - loss: 46.4678
10/145 [=>............................] - ETA: 0s - loss: 49.9576
21/145 [===>..........................] - ETA: 0s - loss: 44.4478
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145/145 [==============================] - 5s 17ms/step - loss: 16.96422024-05-06 19:15:39.198894: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
145/145 [==============================] - 6s 22ms/step - loss: 16.9642 - val_loss: 1.2081
Epoch 2/20
1/145 [..............................] - ETA: 1s - loss: 5.5795
9/145 [>.............................] - ETA: 0s - loss: 6.7267
19/145 [==>...........................] - ETA: 0s - loss: 6.5399
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61/145 [===========>..................] - ETA: 0s - loss: 6.1715
72/145 [=============>................] - ETA: 0s - loss: 6.0768
82/145 [===============>..............] - ETA: 0s - loss: 5.9246
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104/145 [====================>.........] - ETA: 0s - loss: 5.7398
115/145 [======================>.......] - ETA: 0s - loss: 5.6460
126/145 [=========================>....] - ETA: 0s - loss: 5.5552
135/145 [==========================>...] - ETA: 0s - loss: 5.4703
144/145 [============================>.] - ETA: 0s - loss: 5.4012
145/145 [==============================] - 1s 5ms/step - loss: 5.4004
145/145 [==============================] - 1s 6ms/step - loss: 5.4004 - val_loss: 0.8976
Epoch 3/20
1/145 [..............................] - ETA: 1s - loss: 3.9111
9/145 [>.............................] - ETA: 0s - loss: 4.4293
19/145 [==>...........................] - ETA: 0s - loss: 4.1524
30/145 [=====>........................] - ETA: 0s - loss: 4.0496
41/145 [=======>......................] - ETA: 0s - loss: 3.9257
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67/145 [============>.................] - ETA: 0s - loss: 3.8377
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108/145 [=====================>........] - ETA: 0s - loss: 3.7306
119/145 [=======================>......] - ETA: 0s - loss: 3.6962
130/145 [=========================>....] - ETA: 0s - loss: 3.6527
141/145 [============================>.] - ETA: 0s - loss: 3.6156
145/145 [==============================] - 1s 5ms/step - loss: 3.6040
145/145 [==============================] - 1s 6ms/step - loss: 3.6040 - val_loss: 0.6919
Epoch 4/20
1/145 [..............................] - ETA: 1s - loss: 2.6878
9/145 [>.............................] - ETA: 0s - loss: 2.9561
19/145 [==>...........................] - ETA: 0s - loss: 2.8511
29/145 [=====>........................] - ETA: 0s - loss: 2.7951
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51/145 [=========>....................] - ETA: 0s - loss: 2.7252
61/145 [===========>..................] - ETA: 0s - loss: 2.6868
69/145 [=============>................] - ETA: 0s - loss: 2.7536
79/145 [===============>..............] - ETA: 0s - loss: 2.6883
90/145 [=================>............] - ETA: 0s - loss: 2.6733
101/145 [===================>..........] - ETA: 0s - loss: 2.6547
112/145 [======================>.......] - ETA: 0s - loss: 2.6215
123/145 [========================>.....] - ETA: 0s - loss: 2.5796
133/145 [==========================>...] - ETA: 0s - loss: 2.5634
144/145 [============================>.] - ETA: 0s - loss: 2.5373
145/145 [==============================] - 1s 5ms/step - loss: 2.5369
145/145 [==============================] - 1s 6ms/step - loss: 2.5369 - val_loss: 0.5816
Epoch 5/20
1/145 [..............................] - ETA: 1s - loss: 2.6723
9/145 [>.............................] - ETA: 0s - loss: 2.4941
20/145 [===>..........................] - ETA: 0s - loss: 2.2869
30/145 [=====>........................] - ETA: 0s - loss: 2.2124
41/145 [=======>......................] - ETA: 0s - loss: 2.1710
51/145 [=========>....................] - ETA: 0s - loss: 2.1798
61/145 [===========>..................] - ETA: 0s - loss: 2.1802
72/145 [=============>................] - ETA: 0s - loss: 2.1622
83/145 [================>.............] - ETA: 0s - loss: 2.1755
94/145 [==================>...........] - ETA: 0s - loss: 2.1407
105/145 [====================>.........] - ETA: 0s - loss: 2.1034
115/145 [======================>.......] - ETA: 0s - loss: 2.0663
126/145 [=========================>....] - ETA: 0s - loss: 2.0341
137/145 [===========================>..] - ETA: 0s - loss: 2.0106
145/145 [==============================] - 1s 5ms/step - loss: 1.9963
145/145 [==============================] - 1s 6ms/step - loss: 1.9963 - val_loss: 0.6800
Epoch 6/20
1/145 [..............................] - ETA: 0s - loss: 1.6451
10/145 [=>............................] - ETA: 0s - loss: 1.8759
20/145 [===>..........................] - ETA: 0s - loss: 1.8837
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60/145 [===========>..................] - ETA: 0s - loss: 1.7178
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103/145 [====================>.........] - ETA: 0s - loss: 1.6700
114/145 [======================>.......] - ETA: 0s - loss: 1.6623
125/145 [========================>.....] - ETA: 0s - loss: 1.6400
136/145 [===========================>..] - ETA: 0s - loss: 1.6359
145/145 [==============================] - 1s 5ms/step - loss: 1.6125
145/145 [==============================] - 1s 6ms/step - loss: 1.6125 - val_loss: 0.4125
Epoch 7/20
1/145 [..............................] - ETA: 1s - loss: 1.3797
8/145 [>.............................] - ETA: 1s - loss: 1.4366
18/145 [==>...........................] - ETA: 0s - loss: 1.4155
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110/145 [=====================>........] - ETA: 0s - loss: 1.4300
121/145 [========================>.....] - ETA: 0s - loss: 1.4109
132/145 [==========================>...] - ETA: 0s - loss: 1.4028
142/145 [============================>.] - ETA: 0s - loss: 1.4008
145/145 [==============================] - 1s 5ms/step - loss: 1.3981
145/145 [==============================] - 1s 6ms/step - loss: 1.3981 - val_loss: 0.2959
Epoch 8/20
1/145 [..............................] - ETA: 1s - loss: 0.9104
10/145 [=>............................] - ETA: 0s - loss: 1.2762
21/145 [===>..........................] - ETA: 0s - loss: 1.3011
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53/145 [=========>....................] - ETA: 0s - loss: 1.2516
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85/145 [================>.............] - ETA: 0s - loss: 1.2243
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107/145 [=====================>........] - ETA: 0s - loss: 1.2321
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128/145 [=========================>....] - ETA: 0s - loss: 1.2260
139/145 [===========================>..] - ETA: 0s - loss: 1.2180
145/145 [==============================] - 1s 5ms/step - loss: 1.2225
145/145 [==============================] - 1s 6ms/step - loss: 1.2225 - val_loss: 0.3566
Epoch 9/20
1/145 [..............................] - ETA: 1s - loss: 0.9176
10/145 [=>............................] - ETA: 0s - loss: 1.1608
20/145 [===>..........................] - ETA: 0s - loss: 1.1806
31/145 [=====>........................] - ETA: 0s - loss: 1.1855
42/145 [=======>......................] - ETA: 0s - loss: 1.1916
53/145 [=========>....................] - ETA: 0s - loss: 1.1903
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86/145 [================>.............] - ETA: 0s - loss: 1.1535
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108/145 [=====================>........] - ETA: 0s - loss: 1.1369
119/145 [=======================>......] - ETA: 0s - loss: 1.1377
130/145 [=========================>....] - ETA: 0s - loss: 1.1367
140/145 [===========================>..] - ETA: 0s - loss: 1.1345
145/145 [==============================] - 1s 5ms/step - loss: 1.1392
145/145 [==============================] - 1s 6ms/step - loss: 1.1392 - val_loss: 0.2703
Epoch 10/20
1/145 [..............................] - ETA: 0s - loss: 0.7177
10/145 [=>............................] - ETA: 0s - loss: 0.9945
21/145 [===>..........................] - ETA: 0s - loss: 1.0305
32/145 [=====>........................] - ETA: 0s - loss: 1.0724
42/145 [=======>......................] - ETA: 0s - loss: 1.0901
53/145 [=========>....................] - ETA: 0s - loss: 1.0879
63/145 [============>.................] - ETA: 0s - loss: 1.0833
74/145 [==============>...............] - ETA: 0s - loss: 1.0786
84/145 [================>.............] - ETA: 0s - loss: 1.0907
95/145 [==================>...........] - ETA: 0s - loss: 1.0924
106/145 [====================>.........] - ETA: 0s - loss: 1.0935
117/145 [=======================>......] - ETA: 0s - loss: 1.0880
128/145 [=========================>....] - ETA: 0s - loss: 1.0842
139/145 [===========================>..] - ETA: 0s - loss: 1.0795
145/145 [==============================] - 1s 5ms/step - loss: 1.0823
145/145 [==============================] - 1s 6ms/step - loss: 1.0823 - val_loss: 0.2235
Epoch 11/20
1/145 [..............................] - ETA: 1s - loss: 0.9234
10/145 [=>............................] - ETA: 0s - loss: 1.0393
21/145 [===>..........................] - ETA: 0s - loss: 1.0970
32/145 [=====>........................] - ETA: 0s - loss: 1.0911
43/145 [=======>......................] - ETA: 0s - loss: 1.0702
53/145 [=========>....................] - ETA: 0s - loss: 1.0555
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86/145 [================>.............] - ETA: 0s - loss: 1.0333
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108/145 [=====================>........] - ETA: 0s - loss: 1.0315
119/145 [=======================>......] - ETA: 0s - loss: 1.0294
130/145 [=========================>....] - ETA: 0s - loss: 1.0150
141/145 [============================>.] - ETA: 0s - loss: 1.0074
145/145 [==============================] - 1s 5ms/step - loss: 1.0092
145/145 [==============================] - 1s 6ms/step - loss: 1.0092 - val_loss: 0.1606
Epoch 12/20
1/145 [..............................] - ETA: 1s - loss: 1.0959
10/145 [=>............................] - ETA: 0s - loss: 1.0997
20/145 [===>..........................] - ETA: 0s - loss: 1.1739
31/145 [=====>........................] - ETA: 0s - loss: 1.1158
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51/145 [=========>....................] - ETA: 0s - loss: 1.0877
61/145 [===========>..................] - ETA: 0s - loss: 1.0639
72/145 [=============>................] - ETA: 0s - loss: 1.0319
83/145 [================>.............] - ETA: 0s - loss: 1.0050
94/145 [==================>...........] - ETA: 0s - loss: 0.9928
106/145 [====================>.........] - ETA: 0s - loss: 0.9815
117/145 [=======================>......] - ETA: 0s - loss: 0.9718
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134/145 [==========================>...] - ETA: 0s - loss: 0.9554
143/145 [============================>.] - ETA: 0s - loss: 0.9475
145/145 [==============================] - 1s 5ms/step - loss: 0.9474
145/145 [==============================] - 1s 6ms/step - loss: 0.9474 - val_loss: 0.1331
Epoch 13/20
1/145 [..............................] - ETA: 1s - loss: 1.2933
10/145 [=>............................] - ETA: 0s - loss: 0.9642
21/145 [===>..........................] - ETA: 0s - loss: 1.0113
32/145 [=====>........................] - ETA: 0s - loss: 0.9469
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87/145 [=================>............] - ETA: 0s - loss: 0.9354
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120/145 [=======================>......] - ETA: 0s - loss: 0.9331
131/145 [==========================>...] - ETA: 0s - loss: 0.9292
142/145 [============================>.] - ETA: 0s - loss: 0.9305
145/145 [==============================] - 1s 5ms/step - loss: 0.9306
145/145 [==============================] - 1s 6ms/step - loss: 0.9306 - val_loss: 0.0494
Epoch 14/20
1/145 [..............................] - ETA: 1s - loss: 1.3148
9/145 [>.............................] - ETA: 0s - loss: 0.9497
19/145 [==>...........................] - ETA: 0s - loss: 0.9796
29/145 [=====>........................] - ETA: 0s - loss: 0.9708
40/145 [=======>......................] - ETA: 0s - loss: 1.0004
51/145 [=========>....................] - ETA: 0s - loss: 0.9955
62/145 [===========>..................] - ETA: 0s - loss: 1.0031
73/145 [==============>...............] - ETA: 0s - loss: 0.9821
84/145 [================>.............] - ETA: 0s - loss: 1.0005
95/145 [==================>...........] - ETA: 0s - loss: 0.9823
106/145 [====================>.........] - ETA: 0s - loss: 0.9760
117/145 [=======================>......] - ETA: 0s - loss: 0.9699
128/145 [=========================>....] - ETA: 0s - loss: 0.9751
139/145 [===========================>..] - ETA: 0s - loss: 0.9703
145/145 [==============================] - 1s 5ms/step - loss: 0.9719
145/145 [==============================] - 1s 6ms/step - loss: 0.9719 - val_loss: 0.0665
Epoch 15/20
1/145 [..............................] - ETA: 0s - loss: 1.0839
10/145 [=>............................] - ETA: 0s - loss: 0.9706
21/145 [===>..........................] - ETA: 0s - loss: 0.9496
32/145 [=====>........................] - ETA: 0s - loss: 0.9541
43/145 [=======>......................] - ETA: 0s - loss: 1.0027
54/145 [==========>...................] - ETA: 0s - loss: 1.0247
65/145 [============>.................] - ETA: 0s - loss: 1.0258
76/145 [==============>...............] - ETA: 0s - loss: 1.0422
87/145 [=================>............] - ETA: 0s - loss: 1.0377
97/145 [===================>..........] - ETA: 0s - loss: 1.0383
108/145 [=====================>........] - ETA: 0s - loss: 1.0199
119/145 [=======================>......] - ETA: 0s - loss: 1.0224
130/145 [=========================>....] - ETA: 0s - loss: 1.0136
141/145 [============================>.] - ETA: 0s - loss: 0.9983
145/145 [==============================] - 1s 5ms/step - loss: 1.0049
145/145 [==============================] - 1s 6ms/step - loss: 1.0049 - val_loss: 0.0665
Epoch 16/20
1/145 [..............................] - ETA: 1s - loss: 0.7695
10/145 [=>............................] - ETA: 0s - loss: 1.1915
21/145 [===>..........................] - ETA: 0s - loss: 1.1848
32/145 [=====>........................] - ETA: 0s - loss: 1.1462
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54/145 [==========>...................] - ETA: 0s - loss: 1.1031
65/145 [============>.................] - ETA: 0s - loss: 1.0656
76/145 [==============>...............] - ETA: 0s - loss: 1.0481
87/145 [=================>............] - ETA: 0s - loss: 1.0312
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109/145 [=====================>........] - ETA: 0s - loss: 1.0153
116/145 [=======================>......] - ETA: 0s - loss: 1.0050
126/145 [=========================>....] - ETA: 0s - loss: 1.0029
136/145 [===========================>..] - ETA: 0s - loss: 0.9983
145/145 [==============================] - 1s 5ms/step - loss: 0.9961
145/145 [==============================] - 1s 6ms/step - loss: 0.9961 - val_loss: 0.0411
Epoch 17/20
1/145 [..............................] - ETA: 1s - loss: 0.4677
10/145 [=>............................] - ETA: 0s - loss: 1.0510
20/145 [===>..........................] - ETA: 0s - loss: 1.0894
31/145 [=====>........................] - ETA: 0s - loss: 1.0569
41/145 [=======>......................] - ETA: 0s - loss: 1.0424
52/145 [=========>....................] - ETA: 0s - loss: 1.0108
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84/145 [================>.............] - ETA: 0s - loss: 1.0267
90/145 [=================>............] - ETA: 0s - loss: 1.0323
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106/145 [====================>.........] - ETA: 0s - loss: 1.0186
109/145 [=====================>........] - ETA: 0s - loss: 1.0208
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123/145 [========================>.....] - ETA: 0s - loss: 1.0285
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135/145 [==========================>...] - ETA: 0s - loss: 1.0256
141/145 [============================>.] - ETA: 0s - loss: 1.0257
145/145 [==============================] - 1s 7ms/step - loss: 1.0285
145/145 [==============================] - 1s 8ms/step - loss: 1.0285 - val_loss: 0.0414
Epoch 18/20
1/145 [..............................] - ETA: 1s - loss: 1.1976
3/145 [..............................] - ETA: 4s - loss: 1.2672
7/145 [>.............................] - ETA: 3s - loss: 1.3874
16/145 [==>...........................] - ETA: 1s - loss: 1.3621
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144/145 [============================>.] - ETA: 0s - loss: 1.1510
145/145 [==============================] - 1s 6ms/step - loss: 1.1515
145/145 [==============================] - 1s 7ms/step - loss: 1.1515 - val_loss: 0.0455
Epoch 19/20
1/145 [..............................] - ETA: 0s - loss: 1.8353
10/145 [=>............................] - ETA: 0s - loss: 1.4278
20/145 [===>..........................] - ETA: 0s - loss: 1.3412
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145/145 [==============================] - 1s 6ms/step - loss: 1.1385
145/145 [==============================] - 1s 7ms/step - loss: 1.1385 - val_loss: 0.0671
Epoch 20/20
1/145 [..............................] - ETA: 0s - loss: 1.2627
10/145 [=>............................] - ETA: 0s - loss: 2.4640
20/145 [===>..........................] - ETA: 0s - loss: 2.0883
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129/145 [=========================>....] - ETA: 0s - loss: 1.2958
140/145 [===========================>..] - ETA: 0s - loss: 1.2828
145/145 [==============================] - 1s 5ms/step - loss: 1.2791
145/145 [==============================] - 1s 6ms/step - loss: 1.2791 - val_loss: 0.1387kerasPrediction<-model %>% predict(as.matrix(carbonTestingFinal))2024-05-06 19:42:45.149805: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
1/63 [..............................] - ETA: 3s
18/63 [=======>......................] - ETA: 0s
43/63 [===================>..........] - ETA: 0s
63/63 [==============================] - 0s 2ms/step
63/63 [==============================] - 0s 2ms/steprmse=function(x,y){
return((mean(x-y)^2)^0.5)
}
rmse(kerasPrediction,carbonTestLabels)[1] 0.2827712MAE(kerasPrediction,carbonTestLabels)[1] 0.3099322rsquared<-sum((kerasPrediction-carbonTestLabels)^2)/sum((carbonTestLabels-mean(carbonTestLabels))^2)
rsquared[1] 0.7408867library(tfruns)
runs<-tuning_run(
"carbonEmission.R",
flags=list(
learning_rate=c(0.1,0.5,0.01,0.001),
nodes1=c(8,16,32,64,128),
nodes2=c(8,16,32,64,128),
nodes3=c(8,16,32,64,128),
batch_size=c(16,32,64,128),
dropout=c(0.1,0.2,0.3,0.4,0.5),
activation=c("relu")
),sample=0.001
)10,000 total combinations of flags
(sampled to 10 combinations)yTraining run 1/10 (flags = list(0.01, 64, 128, 8, 16, 0.3, "relu"))
Using run directory runs/2024-05-07T00-42-50Z
> FLAGS<- flags(
+ flag_numeric("nodes1", 32),
+ flag_numeric("nodes2", 32),
+ flag_numeric("nodes3", 32),
+ flag_numeric("batch_size",32),
+ .... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes1, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 19:42:52.758485: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0060s vs `on_train_batch_end` time: 0.0102s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0060s vs `on_train_batch_end` time: 0.0102s). Check your callbacks.
2024-05-06 19:42:55.631617: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.451/451 - 5s - loss: 4.5205 - val_loss: 0.4005 - 5s/epoch - 11ms/step
Epoch 2/20
451/451 - 3s - loss: 0.4233 - val_loss: 0.4380 - 3s/epoch - 7ms/step
Epoch 3/20
451/451 - 4s - loss: 0.2020 - val_loss: 0.8268 - 4s/epoch - 10ms/step
Epoch 4/20
451/451 - 3s - loss: 0.1136 - val_loss: 0.8045 - 3s/epoch - 7ms/step
Epoch 5/20
451/451 - 3s - loss: 0.0774 - val_loss: 0.9548 - 3s/epoch - 8ms/step
Epoch 6/20
451/451 - 3s - loss: 0.0675 - val_loss: 0.8364 - 3s/epoch - 7ms/step
Epoch 7/20
451/451 - 3s - loss: 0.0749 - val_loss: 0.6173 - 3s/epoch - 7ms/step
Epoch 8/20
451/451 - 3s - loss: 0.1461 - val_loss: 0.5754 - 3s/epoch - 6ms/step
Epoch 9/20
451/451 - 4s - loss: 0.1720 - val_loss: 0.6730 - 4s/epoch - 9ms/step
Epoch 10/20
451/451 - 4s - loss: 0.1697 - val_loss: 1.0733 - 4s/epoch - 8ms/step
Epoch 11/20
451/451 - 3s - loss: 0.2268 - val_loss: 1.8652 - 3s/epoch - 7ms/step
Epoch 12/20
451/451 - 3s - loss: 0.2462 - val_loss: 3.5136 - 3s/epoch - 6ms/step
Epoch 13/20
451/451 - 3s - loss: 0.3646 - val_loss: 3.4387 - 3s/epoch - 7ms/step
Epoch 14/20
451/451 - 3s - loss: 0.2858 - val_loss: 5.3962 - 3s/epoch - 6ms/step
Epoch 15/20
451/451 - 3s - loss: 0.4880 - val_loss: 5.5874 - 3s/epoch - 6ms/step
Epoch 16/20
451/451 - 3s - loss: 0.2290 - val_loss: 6.1486 - 3s/epoch - 6ms/step
Epoch 17/20
451/451 - 3s - loss: 0.5407 - val_loss: 6.6616 - 3s/epoch - 6ms/step
Epoch 18/20
451/451 - 3s - loss: 0.2750 - val_loss: 5.1347 - 3s/epoch - 6ms/step
Epoch 19/20
451/451 - 3s - loss: 0.7823 - val_loss: 6.0686 - 3s/epoch - 7ms/step
Epoch 20/20
451/451 - 3s - loss: 0.2407 - val_loss: 6.4525 - 3s/epoch - 6ms/step
Run completed: runs/2024-05-07T00-42-50Z
Training run 2/10 (flags = list(0.1, 64, 32, 64, 32, 0.5, "relu"))
Using run directory runs/2024-05-07T00-43-56Z
> FLAGS<- flags(
+ flag_numeric("nodes1", 32),
+ flag_numeric("nodes2", 32),
+ flag_numeric("nodes3", 32),
+ flag_numeric("batch_size",32),
+ .... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes1, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 19:43:59.199273: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0057s vs `on_train_batch_end` time: 0.0093s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0057s vs `on_train_batch_end` time: 0.0093s). Check your callbacks.
2024-05-06 19:44:00.902248: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.226/226 - 5s - loss: 14.3461 - val_loss: 3.2779 - 5s/epoch - 23ms/step
Epoch 2/20
226/226 - 2s - loss: 2.3126 - val_loss: 2.0409 - 2s/epoch - 11ms/step
Epoch 3/20
226/226 - 2s - loss: 1.2771 - val_loss: 1.5950 - 2s/epoch - 8ms/step
Epoch 4/20
226/226 - 2s - loss: 0.8722 - val_loss: 1.2679 - 2s/epoch - 7ms/step
Epoch 5/20
226/226 - 2s - loss: 0.6546 - val_loss: 1.4245 - 2s/epoch - 9ms/step
Epoch 6/20
226/226 - 2s - loss: 0.5112 - val_loss: 1.6146 - 2s/epoch - 10ms/step
Epoch 7/20
226/226 - 2s - loss: 0.4576 - val_loss: 1.4389 - 2s/epoch - 8ms/step
Epoch 8/20
226/226 - 2s - loss: 0.4060 - val_loss: 1.8662 - 2s/epoch - 9ms/step
Epoch 9/20
226/226 - 2s - loss: 0.3691 - val_loss: 1.5593 - 2s/epoch - 9ms/step
Epoch 10/20
226/226 - 2s - loss: 0.3522 - val_loss: 1.6031 - 2s/epoch - 7ms/step
Epoch 11/20
226/226 - 2s - loss: 0.3336 - val_loss: 1.8204 - 2s/epoch - 7ms/step
Epoch 12/20
226/226 - 2s - loss: 0.3520 - val_loss: 2.7458 - 2s/epoch - 10ms/step
Epoch 13/20
226/226 - 2s - loss: 0.3466 - val_loss: 2.5845 - 2s/epoch - 7ms/step
Epoch 14/20
226/226 - 2s - loss: 0.5042 - val_loss: 2.2117 - 2s/epoch - 7ms/step
Epoch 15/20
226/226 - 2s - loss: 0.4170 - val_loss: 2.3097 - 2s/epoch - 9ms/step
Epoch 16/20
226/226 - 2s - loss: 0.7537 - val_loss: 1.5459 - 2s/epoch - 8ms/step
Epoch 17/20
226/226 - 2s - loss: 0.6456 - val_loss: 3.3727 - 2s/epoch - 8ms/step
Epoch 18/20
226/226 - 2s - loss: 0.8647 - val_loss: 2.2160 - 2s/epoch - 9ms/step
Epoch 19/20
226/226 - 2s - loss: 3.1060 - val_loss: 3.0374 - 2s/epoch - 7ms/step
Epoch 20/20
226/226 - 2s - loss: 1.0103 - val_loss: 1.9587 - 2s/epoch - 8ms/step
Run completed: runs/2024-05-07T00-43-56Z
Training run 3/10 (flags = list(0.01, 16, 32, 16, 32, 0.1, "relu"))
Using run directory runs/2024-05-07T00-44-37Z
> FLAGS<- flags(
+ flag_numeric("nodes1", 32),
+ flag_numeric("nodes2", 32),
+ flag_numeric("nodes3", 32),
+ flag_numeric("batch_size",32),
+ .... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes1, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 19:44:38.417896: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0062s vs `on_train_batch_end` time: 0.0115s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0062s vs `on_train_batch_end` time: 0.0115s). Check your callbacks.
2024-05-06 19:44:42.894193: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.226/226 - 5s - loss: 11.3299 - val_loss: 0.8747 - 5s/epoch - 24ms/step
Epoch 2/20
226/226 - 2s - loss: 2.9246 - val_loss: 0.6621 - 2s/epoch - 11ms/step
Epoch 3/20
226/226 - 2s - loss: 1.6391 - val_loss: 0.6023 - 2s/epoch - 10ms/step
Epoch 4/20
226/226 - 2s - loss: 0.9053 - val_loss: 0.7329 - 2s/epoch - 10ms/step
Epoch 5/20
226/226 - 2s - loss: 0.5261 - val_loss: 1.1103 - 2s/epoch - 10ms/step
Epoch 6/20
226/226 - 2s - loss: 0.3653 - val_loss: 1.1065 - 2s/epoch - 7ms/step
Epoch 7/20
226/226 - 2s - loss: 0.3033 - val_loss: 0.9268 - 2s/epoch - 8ms/step
Epoch 8/20
226/226 - 2s - loss: 0.2484 - val_loss: 1.1910 - 2s/epoch - 9ms/step
Epoch 9/20
226/226 - 2s - loss: 0.2362 - val_loss: 1.0927 - 2s/epoch - 9ms/step
Epoch 10/20
226/226 - 2s - loss: 0.2023 - val_loss: 1.1800 - 2s/epoch - 8ms/step
Epoch 11/20
226/226 - 2s - loss: 0.1806 - val_loss: 1.1348 - 2s/epoch - 9ms/step
Epoch 12/20
226/226 - 2s - loss: 0.1748 - val_loss: 0.8917 - 2s/epoch - 8ms/step
Epoch 13/20
226/226 - 2s - loss: 0.1582 - val_loss: 1.3242 - 2s/epoch - 7ms/step
Epoch 14/20
226/226 - 2s - loss: 0.1353 - val_loss: 0.9758 - 2s/epoch - 7ms/step
Epoch 15/20
226/226 - 2s - loss: 0.1355 - val_loss: 1.3543 - 2s/epoch - 8ms/step
Epoch 16/20
226/226 - 2s - loss: 0.1270 - val_loss: 1.8962 - 2s/epoch - 8ms/step
Epoch 17/20
226/226 - 2s - loss: 0.1327 - val_loss: 2.2965 - 2s/epoch - 8ms/step
Epoch 18/20
226/226 - 2s - loss: 0.1413 - val_loss: 1.6279 - 2s/epoch - 8ms/step
Epoch 19/20
226/226 - 2s - loss: 0.1550 - val_loss: 1.5583 - 2s/epoch - 7ms/step
Epoch 20/20
226/226 - 2s - loss: 0.1534 - val_loss: 1.1836 - 2s/epoch - 7ms/step
Run completed: runs/2024-05-07T00-44-37Z
Training run 4/10 (flags = list(0.01, 128, 8, 128, 128, 0.3, "relu"))
Using run directory runs/2024-05-07T00-45-19Z
> FLAGS<- flags(
+ flag_numeric("nodes1", 32),
+ flag_numeric("nodes2", 32),
+ flag_numeric("nodes3", 32),
+ flag_numeric("batch_size",32),
+ .... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes1, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 19:45:22.172741: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0060s vs `on_train_batch_end` time: 0.0094s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0060s vs `on_train_batch_end` time: 0.0094s). Check your callbacks.
2024-05-06 19:45:24.718914: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.57/57 - 3s - loss: 21.1251 - val_loss: 1.5118 - 3s/epoch - 59ms/step
Epoch 2/20
57/57 - 0s - loss: 6.1987 - val_loss: 0.4771 - 458ms/epoch - 8ms/step
Epoch 3/20
57/57 - 1s - loss: 4.4051 - val_loss: 0.2806 - 1s/epoch - 22ms/step
Epoch 4/20
57/57 - 0s - loss: 3.7106 - val_loss: 0.2391 - 442ms/epoch - 8ms/step
Epoch 5/20
57/57 - 1s - loss: 3.0778 - val_loss: 0.1993 - 1s/epoch - 18ms/step
Epoch 6/20
57/57 - 1s - loss: 2.5263 - val_loss: 0.1953 - 743ms/epoch - 13ms/step
Epoch 7/20
57/57 - 1s - loss: 1.7711 - val_loss: 0.1573 - 921ms/epoch - 16ms/step
Epoch 8/20
57/57 - 0s - loss: 0.9103 - val_loss: 0.1378 - 447ms/epoch - 8ms/step
Epoch 9/20
57/57 - 1s - loss: 0.4535 - val_loss: 0.1291 - 728ms/epoch - 13ms/step
Epoch 10/20
57/57 - 0s - loss: 0.2738 - val_loss: 0.1046 - 444ms/epoch - 8ms/step
Epoch 11/20
57/57 - 1s - loss: 0.2211 - val_loss: 0.1144 - 690ms/epoch - 12ms/step
Epoch 12/20
57/57 - 1s - loss: 0.1963 - val_loss: 0.0547 - 740ms/epoch - 13ms/step
Epoch 13/20
57/57 - 0s - loss: 0.1874 - val_loss: 0.0956 - 443ms/epoch - 8ms/step
Epoch 14/20
57/57 - 1s - loss: 0.1646 - val_loss: 0.0446 - 1s/epoch - 18ms/step
Epoch 15/20
57/57 - 1s - loss: 0.1588 - val_loss: 0.0288 - 962ms/epoch - 17ms/step
Epoch 16/20
57/57 - 1s - loss: 0.1845 - val_loss: 0.0203 - 729ms/epoch - 13ms/step
Epoch 17/20
57/57 - 0s - loss: 0.1786 - val_loss: 0.0361 - 458ms/epoch - 8ms/step
Epoch 18/20
57/57 - 1s - loss: 0.2073 - val_loss: 0.1647 - 676ms/epoch - 12ms/step
Epoch 19/20
57/57 - 1s - loss: 0.2029 - val_loss: 0.0998 - 721ms/epoch - 13ms/step
Epoch 20/20
57/57 - 0s - loss: 0.2267 - val_loss: 0.0275 - 439ms/epoch - 8ms/step
Run completed: runs/2024-05-07T00-45-19Z
Training run 5/10 (flags = list(0.01, 8, 32, 32, 16, 0.3, "relu"))
Using run directory runs/2024-05-07T00-45-38Z
> FLAGS<- flags(
+ flag_numeric("nodes1", 32),
+ flag_numeric("nodes2", 32),
+ flag_numeric("nodes3", 32),
+ flag_numeric("batch_size",32),
+ .... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes1, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 19:45:39.575838: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0061s vs `on_train_batch_end` time: 0.0103s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0061s vs `on_train_batch_end` time: 0.0103s). Check your callbacks.
2024-05-06 19:45:44.887951: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.451/451 - 7s - loss: 13.4441 - val_loss: 2.1683 - 7s/epoch - 16ms/step
Epoch 2/20
451/451 - 4s - loss: 1.7129 - val_loss: 1.7100 - 4s/epoch - 8ms/step
Epoch 3/20
451/451 - 3s - loss: 1.0867 - val_loss: 1.6948 - 3s/epoch - 6ms/step
Epoch 4/20
451/451 - 3s - loss: 0.8299 - val_loss: 0.8653 - 3s/epoch - 7ms/step
Epoch 5/20
451/451 - 3s - loss: 0.4791 - val_loss: 1.1193 - 3s/epoch - 6ms/step
Epoch 6/20
451/451 - 3s - loss: 0.4448 - val_loss: 0.5661 - 3s/epoch - 8ms/step
Epoch 7/20
451/451 - 3s - loss: 0.6548 - val_loss: 0.8835 - 3s/epoch - 7ms/step
Epoch 8/20
451/451 - 3s - loss: 1.0771 - val_loss: 1.3313 - 3s/epoch - 6ms/step
Epoch 9/20
451/451 - 3s - loss: 1.1615 - val_loss: 0.6273 - 3s/epoch - 7ms/step
Epoch 10/20
451/451 - 3s - loss: 2.2614 - val_loss: 8.0941 - 3s/epoch - 7ms/step
Epoch 11/20
451/451 - 3s - loss: 1.7852 - val_loss: 29.5401 - 3s/epoch - 7ms/step
Epoch 12/20
451/451 - 3s - loss: 1.7933 - val_loss: 72.5029 - 3s/epoch - 7ms/step
Epoch 13/20
451/451 - 3s - loss: 1.1154 - val_loss: 90.1913 - 3s/epoch - 7ms/step
Epoch 14/20
451/451 - 3s - loss: 1.0355 - val_loss: 124.6499 - 3s/epoch - 7ms/step
Epoch 15/20
451/451 - 3s - loss: 0.7773 - val_loss: 130.9595 - 3s/epoch - 7ms/step
Epoch 16/20
451/451 - 3s - loss: 0.6462 - val_loss: 146.5425 - 3s/epoch - 7ms/step
Epoch 17/20
451/451 - 3s - loss: 0.5830 - val_loss: 135.5791 - 3s/epoch - 7ms/step
Epoch 18/20
451/451 - 3s - loss: 0.8807 - val_loss: 116.1140 - 3s/epoch - 7ms/step
Epoch 19/20
451/451 - 3s - loss: 1.4421 - val_loss: 85.4320 - 3s/epoch - 7ms/step
Epoch 20/20
451/451 - 3s - loss: 1.6305 - val_loss: 68.1109 - 3s/epoch - 7ms/step
Run completed: runs/2024-05-07T00-45-38Z
Training run 6/10 (flags = list(0.1, 16, 128, 64, 16, 0.3, "relu"))
Using run directory runs/2024-05-07T00-46-45Z
> FLAGS<- flags(
+ flag_numeric("nodes1", 32),
+ flag_numeric("nodes2", 32),
+ flag_numeric("nodes3", 32),
+ flag_numeric("batch_size",32),
+ .... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes1, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 19:46:46.473419: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0067s vs `on_train_batch_end` time: 0.0103s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0067s vs `on_train_batch_end` time: 0.0103s). Check your callbacks.
2024-05-06 19:46:51.933581: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.451/451 - 7s - loss: 4.9497 - val_loss: 0.6491 - 7s/epoch - 15ms/step
Epoch 2/20
451/451 - 3s - loss: 1.1165 - val_loss: 1.0126 - 3s/epoch - 8ms/step
Epoch 3/20
451/451 - 3s - loss: 0.5470 - val_loss: 2.5428 - 3s/epoch - 7ms/step
Epoch 4/20
451/451 - 3s - loss: 0.4018 - val_loss: 3.2496 - 3s/epoch - 7ms/step
Epoch 5/20
451/451 - 3s - loss: 0.4970 - val_loss: 10.9413 - 3s/epoch - 7ms/step
Epoch 6/20
451/451 - 3s - loss: 0.6579 - val_loss: 27.3845 - 3s/epoch - 7ms/step
Epoch 7/20
451/451 - 3s - loss: 1.1860 - val_loss: 29.6661 - 3s/epoch - 7ms/step
Epoch 8/20
451/451 - 3s - loss: 1.0036 - val_loss: 40.4824 - 3s/epoch - 6ms/step
Epoch 9/20
451/451 - 3s - loss: 1.8350 - val_loss: 64.6819 - 3s/epoch - 6ms/step
Epoch 10/20
451/451 - 3s - loss: 2.5249 - val_loss: 84.0588 - 3s/epoch - 7ms/step
Epoch 11/20
451/451 - 3s - loss: 0.7133 - val_loss: 116.0839 - 3s/epoch - 6ms/step
Epoch 12/20
451/451 - 3s - loss: 1.6769 - val_loss: 146.3577 - 3s/epoch - 6ms/step
Epoch 13/20
451/451 - 3s - loss: 1.2271 - val_loss: 135.2326 - 3s/epoch - 6ms/step
Epoch 14/20
451/451 - 3s - loss: 4.7599 - val_loss: 44.5545 - 3s/epoch - 6ms/step
Epoch 15/20
451/451 - 3s - loss: 3.2621 - val_loss: 147.3202 - 3s/epoch - 6ms/step
Epoch 16/20
451/451 - 3s - loss: 1.4628 - val_loss: 192.1331 - 3s/epoch - 6ms/step
Epoch 17/20
451/451 - 3s - loss: 6.8850 - val_loss: 217.9560 - 3s/epoch - 6ms/step
Epoch 18/20
451/451 - 3s - loss: 0.6509 - val_loss: 241.0453 - 3s/epoch - 6ms/step
Epoch 19/20
451/451 - 3s - loss: 1.8630 - val_loss: 352.0300 - 3s/epoch - 6ms/step
Epoch 20/20
451/451 - 3s - loss: 2.8394 - val_loss: 425.2216 - 3s/epoch - 6ms/step
Run completed: runs/2024-05-07T00-46-45Z
Training run 7/10 (flags = list(0.1, 64, 32, 128, 64, 0.3, "relu"))
Using run directory runs/2024-05-07T00-47-50Z
> FLAGS<- flags(
+ flag_numeric("nodes1", 32),
+ flag_numeric("nodes2", 32),
+ flag_numeric("nodes3", 32),
+ flag_numeric("batch_size",32),
+ .... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes1, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 19:47:50.751923: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0065s vs `on_train_batch_end` time: 0.0105s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0065s vs `on_train_batch_end` time: 0.0105s). Check your callbacks.
2024-05-06 19:47:54.365512: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.113/113 - 5s - loss: 12.4502 - val_loss: 0.4850 - 5s/epoch - 42ms/step
Epoch 2/20
113/113 - 1s - loss: 2.6941 - val_loss: 0.2250 - 1s/epoch - 12ms/step
Epoch 3/20
113/113 - 1s - loss: 1.6491 - val_loss: 0.1194 - 1s/epoch - 9ms/step
Epoch 4/20
113/113 - 2s - loss: 1.1856 - val_loss: 0.1422 - 2s/epoch - 14ms/step
Epoch 5/20
113/113 - 1s - loss: 0.9046 - val_loss: 0.1761 - 1s/epoch - 12ms/step
Epoch 6/20
113/113 - 1s - loss: 0.7783 - val_loss: 0.2094 - 1s/epoch - 9ms/step
Epoch 7/20
113/113 - 1s - loss: 0.6274 - val_loss: 0.3228 - 1s/epoch - 12ms/step
Epoch 8/20
113/113 - 1s - loss: 0.5675 - val_loss: 0.4505 - 752ms/epoch - 7ms/step
Epoch 9/20
113/113 - 1s - loss: 0.4984 - val_loss: 0.3619 - 1s/epoch - 11ms/step
Epoch 10/20
113/113 - 1s - loss: 0.5197 - val_loss: 0.3234 - 1s/epoch - 11ms/step
Epoch 11/20
113/113 - 1s - loss: 0.4596 - val_loss: 0.6033 - 1s/epoch - 9ms/step
Epoch 12/20
113/113 - 1s - loss: 0.4506 - val_loss: 0.7246 - 1s/epoch - 9ms/step
Epoch 13/20
113/113 - 1s - loss: 0.4010 - val_loss: 0.5564 - 1s/epoch - 9ms/step
Epoch 14/20
113/113 - 1s - loss: 0.3933 - val_loss: 1.0406 - 1s/epoch - 9ms/step
Epoch 15/20
113/113 - 1s - loss: 0.3958 - val_loss: 0.8440 - 1s/epoch - 11ms/step
Epoch 16/20
113/113 - 1s - loss: 0.3832 - val_loss: 1.0860 - 1s/epoch - 10ms/step
Epoch 17/20
113/113 - 1s - loss: 0.3745 - val_loss: 1.7516 - 1s/epoch - 10ms/step
Epoch 18/20
113/113 - 1s - loss: 0.3701 - val_loss: 0.9606 - 1s/epoch - 12ms/step
Epoch 19/20
113/113 - 1s - loss: 0.3326 - val_loss: 1.1989 - 760ms/epoch - 7ms/step
Epoch 20/20
113/113 - 1s - loss: 0.5680 - val_loss: 1.1021 - 1s/epoch - 12ms/step
Run completed: runs/2024-05-07T00-47-50Z
Training run 8/10 (flags = list(0.001, 16, 32, 64, 128, 0.2, "relu"))
Using run directory runs/2024-05-07T00-48-17Z
> FLAGS<- flags(
+ flag_numeric("nodes1", 32),
+ flag_numeric("nodes2", 32),
+ flag_numeric("nodes3", 32),
+ flag_numeric("batch_size",32),
+ .... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes1, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 19:48:20.610911: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0069s vs `on_train_batch_end` time: 0.0627s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0069s vs `on_train_batch_end` time: 0.0627s). Check your callbacks.
2024-05-06 19:48:21.814358: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.57/57 - 5s - loss: 22.3041 - val_loss: 2.8608 - 5s/epoch - 83ms/step
Epoch 2/20
57/57 - 0s - loss: 5.5865 - val_loss: 1.0446 - 455ms/epoch - 8ms/step
Epoch 3/20
57/57 - 1s - loss: 4.1542 - val_loss: 0.6797 - 1s/epoch - 18ms/step
Epoch 4/20
57/57 - 0s - loss: 3.4006 - val_loss: 0.4687 - 447ms/epoch - 8ms/step
Epoch 5/20
57/57 - 0s - loss: 3.0850 - val_loss: 0.4654 - 461ms/epoch - 8ms/step
Epoch 6/20
57/57 - 1s - loss: 2.6984 - val_loss: 0.4543 - 736ms/epoch - 13ms/step
Epoch 7/20
57/57 - 1s - loss: 2.4992 - val_loss: 0.3528 - 1s/epoch - 19ms/step
Epoch 8/20
57/57 - 1s - loss: 2.3194 - val_loss: 0.2989 - 1s/epoch - 18ms/step
Epoch 9/20
57/57 - 1s - loss: 2.2032 - val_loss: 0.1999 - 765ms/epoch - 13ms/step
Epoch 10/20
57/57 - 0s - loss: 1.9163 - val_loss: 0.3407 - 449ms/epoch - 8ms/step
Epoch 11/20
57/57 - 1s - loss: 1.8562 - val_loss: 0.3737 - 737ms/epoch - 13ms/step
Epoch 12/20
57/57 - 1s - loss: 1.7622 - val_loss: 0.2780 - 735ms/epoch - 13ms/step
Epoch 13/20
57/57 - 0s - loss: 1.7513 - val_loss: 0.2511 - 457ms/epoch - 8ms/step
Epoch 14/20
57/57 - 1s - loss: 1.7762 - val_loss: 0.3243 - 757ms/epoch - 13ms/step
Epoch 15/20
57/57 - 1s - loss: 1.6476 - val_loss: 0.2067 - 928ms/epoch - 16ms/step
Epoch 16/20
57/57 - 1s - loss: 1.6757 - val_loss: 0.4440 - 1s/epoch - 18ms/step
Epoch 17/20
57/57 - 1s - loss: 1.6551 - val_loss: 0.4578 - 1s/epoch - 23ms/step
Epoch 18/20
57/57 - 0s - loss: 1.7856 - val_loss: 0.3688 - 449ms/epoch - 8ms/step
Epoch 19/20
57/57 - 0s - loss: 1.6913 - val_loss: 0.2022 - 460ms/epoch - 8ms/step
Epoch 20/20
57/57 - 1s - loss: 1.7090 - val_loss: 0.3874 - 1s/epoch - 18ms/step
Run completed: runs/2024-05-07T00-48-17Z
Training run 9/10 (flags = list(0.1, 32, 16, 64, 64, 0.1, "relu"))
Using run directory runs/2024-05-07T00-48-37Z
> FLAGS<- flags(
+ flag_numeric("nodes1", 32),
+ flag_numeric("nodes2", 32),
+ flag_numeric("nodes3", 32),
+ flag_numeric("batch_size",32),
+ .... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes1, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 19:48:39.568393: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0059s vs `on_train_batch_end` time: 0.0098s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0059s vs `on_train_batch_end` time: 0.0098s). Check your callbacks.
2024-05-06 19:48:41.908953: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.113/113 - 3s - loss: 20.2791 - val_loss: 0.9935 - 3s/epoch - 28ms/step
Epoch 2/20
113/113 - 2s - loss: 3.5698 - val_loss: 0.3433 - 2s/epoch - 15ms/step
Epoch 3/20
113/113 - 1s - loss: 2.7130 - val_loss: 0.2677 - 773ms/epoch - 7ms/step
Epoch 4/20
113/113 - 1s - loss: 2.1445 - val_loss: 0.1247 - 1s/epoch - 9ms/step
Epoch 5/20
113/113 - 1s - loss: 1.7315 - val_loss: 0.2425 - 759ms/epoch - 7ms/step
Epoch 6/20
113/113 - 2s - loss: 1.4882 - val_loss: 0.1598 - 2s/epoch - 15ms/step
Epoch 7/20
113/113 - 1s - loss: 1.3584 - val_loss: 0.1821 - 1s/epoch - 10ms/step
Epoch 8/20
113/113 - 1s - loss: 1.2617 - val_loss: 0.1311 - 1s/epoch - 12ms/step
Epoch 9/20
113/113 - 1s - loss: 1.1346 - val_loss: 0.1783 - 745ms/epoch - 7ms/step
Epoch 10/20
113/113 - 1s - loss: 1.0242 - val_loss: 0.1701 - 1s/epoch - 9ms/step
Epoch 11/20
113/113 - 2s - loss: 0.9074 - val_loss: 0.1820 - 2s/epoch - 19ms/step
Epoch 12/20
113/113 - 1s - loss: 0.8492 - val_loss: 0.1924 - 768ms/epoch - 7ms/step
Epoch 13/20
113/113 - 2s - loss: 0.9142 - val_loss: 0.1555 - 2s/epoch - 14ms/step
Epoch 14/20
113/113 - 1s - loss: 0.9461 - val_loss: 0.2216 - 742ms/epoch - 7ms/step
Epoch 15/20
113/113 - 1s - loss: 0.9353 - val_loss: 0.2312 - 1s/epoch - 9ms/step
Epoch 16/20
113/113 - 1s - loss: 0.8900 - val_loss: 0.2293 - 738ms/epoch - 7ms/step
Epoch 17/20
113/113 - 1s - loss: 0.8228 - val_loss: 0.2521 - 1s/epoch - 9ms/step
Epoch 18/20
113/113 - 2s - loss: 0.7746 - val_loss: 0.3063 - 2s/epoch - 14ms/step
Epoch 19/20
113/113 - 1s - loss: 0.6999 - val_loss: 0.6246 - 925ms/epoch - 8ms/step
Epoch 20/20
113/113 - 1s - loss: 0.7131 - val_loss: 0.3272 - 775ms/epoch - 7ms/step
Run completed: runs/2024-05-07T00-48-37Z
Training run 10/10 (flags = list(0.5, 64, 128, 128, 64, 0.4, "relu"))
Using run directory runs/2024-05-07T00-49-04Z
> FLAGS<- flags(
+ flag_numeric("nodes1", 32),
+ flag_numeric("nodes2", 32),
+ flag_numeric("nodes3", 32),
+ flag_numeric("batch_size",32),
+ .... [TRUNCATED]
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = FLAGS$nodes1, activation = FLAGS$activation, input_shape = dim(carbonTrainingFinal)[2]) %>%
+ layer_dropout(rate .... [TRUNCATED]
> model %>% compile(
+ loss="mse",
+ optimizer=optimizer_adam(lr=FLAGS$learning_rate)
+ )WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.
> model %>%fit( as.matrix(carbonTrainingFinal),
+ carbonTrainingLabels,
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/202024-05-06 19:49:04.833257: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0076s vs `on_train_batch_end` time: 0.0106s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0076s vs `on_train_batch_end` time: 0.0106s). Check your callbacks.
2024-05-06 19:49:08.310975: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.113/113 - 5s - loss: 10.5479 - val_loss: 1.4272 - 5s/epoch - 41ms/step
Epoch 2/20
113/113 - 1s - loss: 2.3952 - val_loss: 1.1559 - 1s/epoch - 13ms/step
Epoch 3/20
113/113 - 1s - loss: 1.5941 - val_loss: 1.0406 - 765ms/epoch - 7ms/step
Epoch 4/20
113/113 - 1s - loss: 1.2588 - val_loss: 1.2104 - 1s/epoch - 12ms/step
Epoch 5/20
113/113 - 1s - loss: 1.1194 - val_loss: 1.2557 - 807ms/epoch - 7ms/step
Epoch 6/20
113/113 - 2s - loss: 0.9635 - val_loss: 0.8202 - 2s/epoch - 16ms/step
Epoch 7/20
113/113 - 1s - loss: 0.7808 - val_loss: 1.2612 - 1s/epoch - 12ms/step
Epoch 8/20
113/113 - 1s - loss: 0.6893 - val_loss: 0.9310 - 792ms/epoch - 7ms/step
Epoch 9/20
113/113 - 1s - loss: 0.8263 - val_loss: 0.8854 - 1s/epoch - 12ms/step
Epoch 10/20
113/113 - 1s - loss: 0.8245 - val_loss: 1.9842 - 773ms/epoch - 7ms/step
Epoch 11/20
113/113 - 2s - loss: 0.6097 - val_loss: 1.6098 - 2s/epoch - 14ms/step
Epoch 12/20
113/113 - 1s - loss: 0.6071 - val_loss: 0.5681 - 1s/epoch - 12ms/step
Epoch 13/20
113/113 - 2s - loss: 1.0043 - val_loss: 2.7418 - 2s/epoch - 13ms/step
Epoch 14/20
113/113 - 1s - loss: 0.9712 - val_loss: 1.6307 - 756ms/epoch - 7ms/step
Epoch 15/20
113/113 - 1s - loss: 1.3167 - val_loss: 6.5994 - 1s/epoch - 12ms/step
Epoch 16/20
113/113 - 1s - loss: 1.0642 - val_loss: 3.7203 - 752ms/epoch - 7ms/step
Epoch 17/20
113/113 - 1s - loss: 1.9458 - val_loss: 3.8968 - 1s/epoch - 9ms/step
Epoch 18/20
113/113 - 1s - loss: 1.0726 - val_loss: 5.1104 - 741ms/epoch - 7ms/step
Epoch 19/20
113/113 - 1s - loss: 1.5860 - val_loss: 3.0719 - 1s/epoch - 9ms/step
Epoch 20/20
113/113 - 1s - loss: 1.7421 - val_loss: 6.8720 - 762ms/epoch - 7ms/step
Run completed: runs/2024-05-07T00-49-04ZRuns
runs=runs[order(runs$metric_val_loss),]
runsData frame: 10 x 25 # ... with 22 more columns:
# flag_nodes1, flag_nodes2, flag_nodes3, flag_batch_size, flag_activation, flag_learning_rate, flag_dropout, epochs, epochs_completed, metrics,
# model, loss_function, optimizer, learning_rate, script, start, end, completed, output, source_code, context, typeview_run(runs$run_dir[1])Warning: incomplete final line found on '/var/folders/lw/zymjkl5d1g34b21y_8l475p80000gn/T//RtmpFb0Bja/file719e16574c3f/source/carbonEmission.R'Warning: incomplete final line found on '/var/folders/lw/zymjkl5d1g34b21y_8l475p80000gn/T//RtmpFb0Bja/file719e16574c3f/source/CarbonEmission.R'dim(carbonTrainingFinal)[1] 7202 75dim(carbonValidationFinal)[1] 799 75carbonTrainingFinal<-rbind(carbonTrainingFinal,carbonValidationFinal)
carbonTrainingLabels<-c(carbonTrainingLabels,carbonValidationLabels)
dim(carbonTrainingFinal)[1] 8001 75BestModel<-keras_model_sequential()%>%
layer_dense(units = 128,activation = "relu",input_shape = dim(carbonTrainingFinal)[2])%>%
layer_dropout(rate=0.3)%>%
layer_dense(units = 8,activation = "relu")%>%
layer_dropout(rate=0.3)%>%
layer_dense(units = 128,activation = "relu")%>%
layer_dropout(rate=0.3)%>%
layer_dense(units = 1)
BestModel %>% compile(
loss="mse",
optimizer=optimizer_adam(lr=0.001)
)WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.
WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.history<-BestModel %>% fit(as.matrix(carbonTrainingFinal),
carbonTrainingLabels,
batch_size=128,
epochs=20,
validation_data=list(as.matrix(carbonTestingFinal),carbonTestingLabels)
)Epoch 1/202024-05-06 19:50:10.098149: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
1/63 [..............................] - ETA: 32s - loss: 56.1525
7/63 [==>...........................] - ETA: 0s - loss: 50.6973
14/63 [=====>........................] - ETA: 0s - loss: 43.2697
24/63 [==========>...................] - ETA: 0s - loss: 32.1539
31/63 [=============>................] - ETA: 0s - loss: 27.5672
39/63 [=================>............] - ETA: 0s - loss: 23.9154
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58/63 [==========================>...] - ETA: 0s - loss: 18.6393
63/63 [==============================] - 1s 9ms/step - loss: 17.73162024-05-06 19:50:10.989452: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
63/63 [==============================] - 2s 20ms/step - loss: 17.7316 - val_loss: 0.3594
Epoch 2/20
1/63 [..............................] - ETA: 0s - loss: 6.0446
8/63 [==>...........................] - ETA: 0s - loss: 6.1043
18/63 [=======>......................] - ETA: 0s - loss: 5.9030
26/63 [===========>..................] - ETA: 0s - loss: 5.7035
33/63 [==============>...............] - ETA: 0s - loss: 5.5879
41/63 [==================>...........] - ETA: 0s - loss: 5.4225
50/63 [======================>.......] - ETA: 0s - loss: 5.2888
60/63 [===========================>..] - ETA: 0s - loss: 5.1492
63/63 [==============================] - 0s 6ms/step - loss: 5.1037
63/63 [==============================] - 1s 9ms/step - loss: 5.1037 - val_loss: 0.4893
Epoch 3/20
1/63 [..............................] - ETA: 0s - loss: 3.7968
6/63 [=>............................] - ETA: 0s - loss: 4.2288
14/63 [=====>........................] - ETA: 0s - loss: 4.3073
23/63 [=========>....................] - ETA: 0s - loss: 4.1040
32/63 [==============>...............] - ETA: 0s - loss: 4.0073
42/63 [===================>..........] - ETA: 0s - loss: 3.9244
51/63 [=======================>......] - ETA: 0s - loss: 3.9132
60/63 [===========================>..] - ETA: 0s - loss: 3.8433
63/63 [==============================] - 0s 6ms/step - loss: 3.8226
63/63 [==============================] - 1s 9ms/step - loss: 3.8226 - val_loss: 0.3493
Epoch 4/20
1/63 [..............................] - ETA: 0s - loss: 3.3050
7/63 [==>...........................] - ETA: 0s - loss: 3.3747
16/63 [======>.......................] - ETA: 0s - loss: 3.3454
24/63 [==========>...................] - ETA: 0s - loss: 3.2853
34/63 [===============>..............] - ETA: 0s - loss: 3.2063
42/63 [===================>..........] - ETA: 0s - loss: 3.1480
49/63 [======================>.......] - ETA: 0s - loss: 3.0933
58/63 [==========================>...] - ETA: 0s - loss: 3.0464
63/63 [==============================] - 0s 6ms/step - loss: 3.0180
63/63 [==============================] - 1s 9ms/step - loss: 3.0180 - val_loss: 0.3182
Epoch 5/20
1/63 [..............................] - ETA: 0s - loss: 2.6332
8/63 [==>...........................] - ETA: 0s - loss: 2.6578
17/63 [=======>......................] - ETA: 0s - loss: 2.6075
27/63 [===========>..................] - ETA: 0s - loss: 2.5429
37/63 [================>.............] - ETA: 0s - loss: 2.4747
47/63 [=====================>........] - ETA: 0s - loss: 2.4145
57/63 [==========================>...] - ETA: 0s - loss: 2.3543
63/63 [==============================] - 0s 6ms/step - loss: 2.3245
63/63 [==============================] - 1s 8ms/step - loss: 2.3245 - val_loss: 0.2665
Epoch 6/20
1/63 [..............................] - ETA: 0s - loss: 2.1989
8/63 [==>...........................] - ETA: 0s - loss: 1.9025
18/63 [=======>......................] - ETA: 0s - loss: 1.8597
27/63 [===========>..................] - ETA: 0s - loss: 1.7908
37/63 [================>.............] - ETA: 0s - loss: 1.7341
47/63 [=====================>........] - ETA: 0s - loss: 1.6634
57/63 [==========================>...] - ETA: 0s - loss: 1.6058
63/63 [==============================] - 0s 6ms/step - loss: 1.5699
63/63 [==============================] - 1s 8ms/step - loss: 1.5699 - val_loss: 0.3017
Epoch 7/20
1/63 [..............................] - ETA: 0s - loss: 1.2788
9/63 [===>..........................] - ETA: 0s - loss: 1.2298
18/63 [=======>......................] - ETA: 0s - loss: 1.2152
28/63 [============>.................] - ETA: 0s - loss: 1.1812
38/63 [=================>............] - ETA: 0s - loss: 1.1151
48/63 [=====================>........] - ETA: 0s - loss: 1.0783
58/63 [==========================>...] - ETA: 0s - loss: 1.0496
63/63 [==============================] - 0s 6ms/step - loss: 1.0329
63/63 [==============================] - 1s 8ms/step - loss: 1.0329 - val_loss: 0.4110
Epoch 8/20
1/63 [..............................] - ETA: 0s - loss: 0.9057
8/63 [==>...........................] - ETA: 0s - loss: 0.8505
18/63 [=======>......................] - ETA: 0s - loss: 0.7783
27/63 [===========>..................] - ETA: 0s - loss: 0.7639
36/63 [================>.............] - ETA: 0s - loss: 0.7573
46/63 [====================>.........] - ETA: 0s - loss: 0.7394
55/63 [=========================>....] - ETA: 0s - loss: 0.7227
63/63 [==============================] - 0s 6ms/step - loss: 0.7049
63/63 [==============================] - 1s 9ms/step - loss: 0.7049 - val_loss: 0.3024
Epoch 9/20
1/63 [..............................] - ETA: 0s - loss: 0.6317
8/63 [==>...........................] - ETA: 0s - loss: 0.6003
18/63 [=======>......................] - ETA: 0s - loss: 0.6017
28/63 [============>.................] - ETA: 0s - loss: 0.6022
38/63 [=================>............] - ETA: 0s - loss: 0.5921
48/63 [=====================>........] - ETA: 0s - loss: 0.5811
58/63 [==========================>...] - ETA: 0s - loss: 0.5761
63/63 [==============================] - 0s 6ms/step - loss: 0.5716
63/63 [==============================] - 1s 9ms/step - loss: 0.5716 - val_loss: 0.4165
Epoch 10/20
1/63 [..............................] - ETA: 0s - loss: 0.4257
7/63 [==>...........................] - ETA: 0s - loss: 0.5176
16/63 [======>.......................] - ETA: 0s - loss: 0.5310
26/63 [===========>..................] - ETA: 0s - loss: 0.5257
35/63 [===============>..............] - ETA: 0s - loss: 0.5265
45/63 [====================>.........] - ETA: 0s - loss: 0.5175
54/63 [========================>.....] - ETA: 0s - loss: 0.5178
62/63 [============================>.] - ETA: 0s - loss: 0.5170
63/63 [==============================] - 0s 6ms/step - loss: 0.5155
63/63 [==============================] - 1s 9ms/step - loss: 0.5155 - val_loss: 0.4760
Epoch 11/20
1/63 [..............................] - ETA: 0s - loss: 0.5481
8/63 [==>...........................] - ETA: 0s - loss: 0.5009
18/63 [=======>......................] - ETA: 0s - loss: 0.4864
28/63 [============>.................] - ETA: 0s - loss: 0.4712
37/63 [================>.............] - ETA: 0s - loss: 0.4750
47/63 [=====================>........] - ETA: 0s - loss: 0.4799
58/63 [==========================>...] - ETA: 0s - loss: 0.4830
63/63 [==============================] - 0s 6ms/step - loss: 0.4824
63/63 [==============================] - 1s 8ms/step - loss: 0.4824 - val_loss: 0.4617
Epoch 12/20
1/63 [..............................] - ETA: 0s - loss: 0.4498
8/63 [==>...........................] - ETA: 0s - loss: 0.4512
18/63 [=======>......................] - ETA: 0s - loss: 0.4672
27/63 [===========>..................] - ETA: 0s - loss: 0.4586
37/63 [================>.............] - ETA: 0s - loss: 0.4754
46/63 [====================>.........] - ETA: 0s - loss: 0.4858
54/63 [========================>.....] - ETA: 0s - loss: 0.4884
63/63 [==============================] - 0s 6ms/step - loss: 0.4905
63/63 [==============================] - 1s 9ms/step - loss: 0.4905 - val_loss: 0.4083
Epoch 13/20
1/63 [..............................] - ETA: 0s - loss: 0.4547
8/63 [==>...........................] - ETA: 0s - loss: 0.4782
17/63 [=======>......................] - ETA: 0s - loss: 0.4875
27/63 [===========>..................] - ETA: 0s - loss: 0.4907
37/63 [================>.............] - ETA: 0s - loss: 0.4954
45/63 [====================>.........] - ETA: 0s - loss: 0.4970
55/63 [=========================>....] - ETA: 0s - loss: 0.5106
63/63 [==============================] - 0s 6ms/step - loss: 0.5178
63/63 [==============================] - 1s 9ms/step - loss: 0.5178 - val_loss: 0.3120
Epoch 14/20
1/63 [..............................] - ETA: 0s - loss: 0.4834
9/63 [===>..........................] - ETA: 0s - loss: 0.6559
19/63 [========>.....................] - ETA: 0s - loss: 0.6414
29/63 [============>.................] - ETA: 0s - loss: 0.6487
39/63 [=================>............] - ETA: 0s - loss: 0.6533
49/63 [======================>.......] - ETA: 0s - loss: 0.6356
59/63 [===========================>..] - ETA: 0s - loss: 0.6320
63/63 [==============================] - 0s 5ms/step - loss: 0.6281
63/63 [==============================] - 1s 8ms/step - loss: 0.6281 - val_loss: 0.5378
Epoch 15/20
1/63 [..............................] - ETA: 0s - loss: 0.6601
9/63 [===>..........................] - ETA: 0s - loss: 0.6684
17/63 [=======>......................] - ETA: 0s - loss: 0.7051
27/63 [===========>..................] - ETA: 0s - loss: 0.6947
37/63 [================>.............] - ETA: 0s - loss: 0.6975
47/63 [=====================>........] - ETA: 0s - loss: 0.7048
56/63 [=========================>....] - ETA: 0s - loss: 0.7048
63/63 [==============================] - 0s 6ms/step - loss: 0.7108
63/63 [==============================] - 1s 8ms/step - loss: 0.7108 - val_loss: 0.2581
Epoch 16/20
1/63 [..............................] - ETA: 0s - loss: 0.7801
8/63 [==>...........................] - ETA: 0s - loss: 0.6919
18/63 [=======>......................] - ETA: 0s - loss: 0.7125
27/63 [===========>..................] - ETA: 0s - loss: 0.7137
37/63 [================>.............] - ETA: 0s - loss: 0.7245
47/63 [=====================>........] - ETA: 0s - loss: 0.7272
56/63 [=========================>....] - ETA: 0s - loss: 0.7218
63/63 [==============================] - 0s 6ms/step - loss: 0.7238
63/63 [==============================] - 1s 8ms/step - loss: 0.7238 - val_loss: 0.7199
Epoch 17/20
1/63 [..............................] - ETA: 0s - loss: 0.6796
9/63 [===>..........................] - ETA: 0s - loss: 0.6985
19/63 [========>.....................] - ETA: 0s - loss: 0.7236
28/63 [============>.................] - ETA: 0s - loss: 0.7255
38/63 [=================>............] - ETA: 0s - loss: 0.7247
48/63 [=====================>........] - ETA: 0s - loss: 0.7379
58/63 [==========================>...] - ETA: 0s - loss: 0.7495
63/63 [==============================] - 0s 6ms/step - loss: 0.7535
63/63 [==============================] - 1s 8ms/step - loss: 0.7535 - val_loss: 0.7899
Epoch 18/20
1/63 [..............................] - ETA: 0s - loss: 0.7956
9/63 [===>..........................] - ETA: 0s - loss: 0.8208
19/63 [========>.....................] - ETA: 0s - loss: 0.8132
29/63 [============>.................] - ETA: 0s - loss: 0.8473
39/63 [=================>............] - ETA: 0s - loss: 0.8946
49/63 [======================>.......] - ETA: 0s - loss: 0.9325
59/63 [===========================>..] - ETA: 0s - loss: 0.9543
63/63 [==============================] - 0s 6ms/step - loss: 0.9532
63/63 [==============================] - 1s 8ms/step - loss: 0.9532 - val_loss: 1.1574
Epoch 19/20
1/63 [..............................] - ETA: 0s - loss: 0.9332
9/63 [===>..........................] - ETA: 0s - loss: 1.0345
19/63 [========>.....................] - ETA: 0s - loss: 1.0831
29/63 [============>.................] - ETA: 0s - loss: 1.0410
39/63 [=================>............] - ETA: 0s - loss: 1.0398
49/63 [======================>.......] - ETA: 0s - loss: 1.0370
59/63 [===========================>..] - ETA: 0s - loss: 1.0292
63/63 [==============================] - 0s 5ms/step - loss: 1.0303
63/63 [==============================] - 1s 9ms/step - loss: 1.0303 - val_loss: 0.8911
Epoch 20/20
1/63 [..............................] - ETA: 0s - loss: 0.8411
8/63 [==>...........................] - ETA: 0s - loss: 0.9346
18/63 [=======>......................] - ETA: 0s - loss: 0.9226
28/63 [============>.................] - ETA: 0s - loss: 0.9545
38/63 [=================>............] - ETA: 0s - loss: 1.0284
48/63 [=====================>........] - ETA: 0s - loss: 1.0479
58/63 [==========================>...] - ETA: 0s - loss: 1.0510
63/63 [==============================] - 0s 6ms/step - loss: 1.0448
63/63 [==============================] - 1s 8ms/step - loss: 1.0448 - val_loss: 1.6152predictBestModel<-model %>% predict(as.matrix(carbonTestingFinal))2024-05-06 19:50:31.488130: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
1/63 [..............................] - ETA: 4s
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63/63 [==============================] - 0s 3ms/step
63/63 [==============================] - 0s 3ms/steprmse=function(x,y){
return((mean(x-y)^2)^0.5)
}
rmse(predictBestModel,carbonTestingLabels)[1] 0.2827712MAE(predictBestModel,carbonTestingLabels)[1] 0.3099322rsquaredBest<-sum((predictBestModel-carbonTestingLabels)^2)/sum((carbonTestingLabels-mean(carbonTestingLabels))^2)
rsquaredBest[1] 0.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